NOISE ROBUST ALGORITHMS TO IMPROVE CELL PHONE SPEECH INTELLIGIBILITY FOR THE HEARING IMPAIRED

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1 NOISE ROBUST ALGORITHMS TO IMPROVE CELL PHONE SPEECH INTELLIGIBILITY FOR THE HEARING IMPAIRED By MEENA RAMANI A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA

2 c 2008 Meena Ramani 2

3 To Appapa, Ammama, Appa, Amma and Hari I dedicate this dissertation to my incredible family who have been a constant source of support and inspiration. 3

4 ACKNOWLEDGMENTS I would like to thank my advisor Dr. John G. Harris for his encouragement, patience and guidance. He taught me to ask the right questions and get to the root of the problem and that is something I will always be grateful for. I also thank him for making the hybrid group a home away from home for all of us. I would like to thank Dr. Alice E. Holmes for meeting with me every week and helping me understand the fascinating field of audiology. I also thank Dr. Holmes for access to the Shands speech and hearing clinic where I met amazing people who further strengthened my resolve to work on hearing loss compensation. I would like to thank Dr. Hans van Oostrom and Dr. Clint Slatton for being part of my committee and providing me with helpful insights. I would like to thank the Motorola iden group for funding the research in Chapters 2 and 3. Over the course of my inter-disciplinary research, I had the opportunity to work with several audiology students who have helped me look at hearing loss from a non-engineering perspective. I thank Sharon Powell, Ryan Baker, Shari Kwon and Brittany Sakowicz for that. I also thank them for helping me run the subjective evaluation tests and for helping me collect the hearing aid fitting data. I feel extremely blessed to be part of the hybrid group where I get to interact with brilliant people on a day to day basis. Apart from being extremely knowledgeable researchers, they are also some of the nicest people I have met. I thank Kwansun, Xiaoxiang, Jeremy, Ismail, Mark, Harsha, Du, Christy and the many others for making my PhD life extra special. Finally, I would like to thank my family for their unwavering faith in me. 4

5 TABLE OF CONTENTS page ACKNOWLEDGMENTS LIST OF TABLES LIST OF FIGURES ABSTRACT CHAPTER 1 INTRODUCTION Sensorineural Hearing Impairment Causes of Sensorineural Hearing Loss Perceptual Measure of Sensorineural Hearing Loss Characteristics of Sensorineural Hearing Loss Modeling Sensorineural Hearing Loss Speech Intelligibility and Quality Factors Influencing Speech Intelligibility and Quality Speech Intelligibility Measures Speech Quality Measures Cell Phone Speech Intelligibility HEARING LOSS COMPENSATION ALGORITHMS Review of Existing Hearing Loss Compensation Algorithms Threshold-Only Gain Prescription Procedures Suprathreshold Gain Prescription Procedures Development of Recruitment Based Compensation Parameter Analysis of RBC Dynamic Constants of Compression Filter Bank Analysis Real-Time Implementation Issues Performance Analysis of the RBC Algorithm Performance of Algorithm in Terms of Speech Quality Performance of Algorithm in terms of Speech Intelligibility Summary NOISE ROBUST HEARING ENHANCEMENT ALGORITHMS Effects of Noise on Cell Phone Speech Development of Noise Robust Recruitment Based Compensation Single Microphone Noise Estimation Calculating the Noise Masking Threshold Derivation of Noise Robust Recruitment Based Compensation

6 3.3 Performance Analysis of the NR-RBC Algorithm Performance of Algorithm in Terms of Speech Quality Performance of Algorithm in terms of Speech Intelligibility Summary ACCLIMATIZATION MODELING FOR THE AIDED HEARING IMPAIRED Development of the Fitting Satisfaction Scale Hearing Aid Fitting Data Hearing Aid Fitting Data Collection Multi-Session Hearing Aid Fitting Data Analysis Modeling the Acclimatization Effect Performance Analysis of Model Summary CONCLUSIONS APPENDIX A SURVEY OF HEARING-IMPAIRED CELL PHONE USERS A.1 Participants A.2 Results A.2.1 Cell Phone Usage A.2.2 Electromagnetic Interference A.2.3 Cell Phone Speech and Ringer Level A.2.4 Summary and Conclusions B CELL PHONE HEARING EVALUATION QUESTIONNAIRE C ANALYSIS OF THE FOCUS GROUP DISCUSSIONS C.1 Participants C.2 Focus Group Main Themes C.2.1 Aided Cell Phone Listening Problems C.2.2 Ideal Hearing Aid Compatible Cell Phone C.2.3 Comments on a Cell Phone Assistive Listening Device D PHYSIOLOGY OF HEARING REFERENCES BIOGRAPHICAL SKETCH

7 Table LIST OF TABLES page 1-1 Mean opinion score 5 point scale The k f constant for POGO The k f constant for NAL Sources of cell phone noise and noise-reduction methods Critical bands and FFT bins Speech intelligibility based fitting satisfaction scale Phonak hearing aid fitting parameters

8 Figure LIST OF FIGURES page 1-1 Effects of aging on hearing thresholds Matlab audiogram graphic user interface (GUI) Decreased audibility characteristic of sensorineural hearing loss (SNHL) Decreased dynamic range characteristic of SNHL Decreased frequency resolution characteristic of SNHL Decreased temporal resolution characteristic of SNHL Spectrograms of cell phone speech for normal-hearing and simulated SNHL Simulated SNHL model Speech intelligibility (SI) as a function of bandwidth Hearing in noise test (HINT) Matlab GUI Speaker response for the Motorola i Mean opinion score (MOS) speech quality ratings for cell phone vocoders Nature of cell phone hearing problems Classification of existing hearing aid fitting methods Gains prescribed by the Fig6 method Input-Output curve at 2 khz obtained from the visual input output locator Variation of desired sensation level (DSL) prescribed gain with hearing loss Recruitment based compensation system Computation of gain based on loudness recruitment Estimated dependence of recruitment range on hearing loss Compression input-output and gain curves Effect of variation of filter bank size on speech intelligibility Average MOS scores for the hearing-impaired Audiogram on the phone Java midlet Audiograms of all the hearing-impaired listeners

9 2-13 Hearing loss simulation system The PESQ objective speech quality score for normal-hearing and hearing-impaired Spectrogram of SNHL and linear-amplified speech Spectrogram of normal-hearing and linear-amplified speech Average MOS scores for the hearing-impaired Average MOS scores for the normal-hearing Speech intelligibility index (SII) scores for normal-hearing as a function of SNR Average HINT scores of the hearing-impaired for wide band speech Average HINT scores of the hearing-impaired for cell phone speech Average HINT scores of the normal-hearing for cell phone speech Noise robust recruitment based compensation (NR-RBC) system The PESQ objective speech quality score for various HA fitting algorithms Spectrogram of SNHL and linear-amplified speech Spectrogram of normal-hearing and linear-amplified speech Average NR-RBC MOS scores for the hearing-impaired listener Average NR-RBC MOS scores for the normal-hearing listener The SII scores for simulated normal-hearing as a function of SNR The HINT scores for NR-RBC for hearing-impaired The HINT scores for NR-RBC for normal-hearing Comparison of Claro gain parameters from initial to final fitting Comparison of Savia gain parameters from initial to final fitting Comparison of Savia compression parameters from initial to final fitting Comparison of Savia compression parameters from initial to final fitting Phonak Savia maximum trend in fitting parameter variation Phonak Claro maximum trend in fitting parameter variation Phonak Extra maximum trend in fitting parameter variation Phonak Valeo maximum trend in fitting parameter variation

10 4-9 Phonak Eleva maximum trend in fitting parameter variation Phonak Perseo maximum trend in fitting parameter variation Structure of the MLP used to model multi-session fitting trends Phonak Savia neural network modeling results for 40dB gain parameter Phonak Savia neural network modeling results for 60dB gain parameter Phonak Savia neural network modeling results for 80dB gain parameter Phonak Savia neural network modeling results for CR parameter Phonak Savia neural network modeling results for TK parameter Phonak Savia neural network modeling results for MPO parameter A-1 Degree of hearing impairment A-2 Degree of hearing impairment for survey participants D-1 Structure of the human ear D-2 Organ of corti D-3 Electron micrograph of the organ of corti D-4 Frequency sensitivity of the basilar membrane

11 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy NOISE ROBUST ALGORITHMS TO IMPROVE CELL PHONE SPEECH INTELLIGIBILITY FOR THE HEARING IMPAIRED Chair: John G. Harris Major: Electrical and Computer Engineering By Meena Ramani May 2008 Cell phone speech can lead to a difficult listening environment because of the environmental noise, the reduced bandwidth, the packet drop offs and the vocoder artifacts. This is especially true for hearing-impaired listeners who require a 9 db improvement in signal to noise ratio (SNR) compared to normal-hearing listeners in order to understand speech in noise. This research explored various means to improve cell phone speech intelligibility for the hearing-impaired and resulted in the development of three novel hearing enhancement algorithms. The first algorithm developed by us is the recruitment based compensation (RBC) fitting method. RBC is a hearing enhancement algorithm aimed at improving speech intelligibility (SI) for unaided listeners with sensorineural hearing loss. It is a fitting algorithm which adjusts the gain parameters of the cell phone based on the individuals threshold of hearing. It provides multiple band gain and compression to make cell phone speech audible and within the reduced dynamic range of the hearing-impaired individual. Subjective hearing in noise tests (HINT) run on hearing-impaired subjects reveal that RBC shows a 15 db improvement in SNR when compared to linear amplification which is typical of the cell phone volume control. RBC also shows a 6 db improvement in SNR when compared to the desired sensation level (DSL) fitting method which is a popular audiology option. 11

12 The second algorithm developed by us is the noise robust recruitment based compensation (NR-RBC) algorithm. NR-RBC is derived from RBC but uses the masked thresholds in noise instead of the thresholds in quiet. NR-RBC provides hearing loss compensation and automatic volume control in noisy environments. The objective speech intelligibility index (SII) scores indicate that NR-RBC has high speech usage when compared to all the other fitting methods. Both RBC and NR-RBC received a speech quality mean opinion score (MOS) of Good. Though RBC and NR-RBC were designed with the hearing-impaired in mind the algorithm proves beneficial to the normal-hearing person with slight modifications. This resulted in a 3 db improvement in SNR when compared to DSL using RBC, a 13 db improvement in SNR using NR-RBC and a speech quality rating of Good. For the aided hearing-impaired population, the hearing aid fitting acclimatization method was developed to improve speech intelligibility. Acclimatization occurs because of the plasticity of the auditory cortex. Acclimatization modeling was carried out using neural networks which were trained with multi-session Phonak hearing aid fitting data. This method is to be used in conjunction with existing hearing loss fitting algorithms and predicts the effect of hearing aid acclimatization. The mean square error (MSE) between the predicted values and the optimal values averaged across the parameters is lower than with the initial settings. 12

13 CHAPTER 1 INTRODUCTION The sense of hearing plays a pivotal role in human interaction and communication. Acoustic pressure waves are transduced by the cochlea into electrical neural signals which are processed by the brain to provide a meaningful cognitive experience. Hearing impairment can reduce the ability to communicate successfully. The inability of being able to understand what is being said can result in social and emotional isolation [1]. The telephone, one of the most important inventions of the 19th century, was the result of Alexander Graham Bell s work on communication devices for the hearing-impaired. Telephones have now become an integral part of human communication and provide easy means of long-distance communication. The invention of the wireless cell phone has further lead to an ease in communication. Cell phones are the modern day Swiss Army knives and are packed with a myriad of hardware and software functionalities. As of June 2007, there are 243 million [2] cell phone subscribers in the United States and this number is growing. In the United States alone there are 28 million [3] people who are hearing-impaired. Yet less than 8% of them use hearing aids though they could obtain significant improvement with them. This is mainly because of the high costs and the stigma attached to using hearing aids. Studies have shown that hearing-impaired listeners require a 9 db improvement in signal to noise ratio (SNR) [4] when compared to normal-hearing listeners in order to understand conversational speech. Hearing aids can help satisfy this requirement to a certain extent. Unfortunately hearing aids and cell phones are not completely compatible because of electromagnetic interference (EM) [5]. The amount of interference depends on the amount of radio-frequency (RF) emission produced by the particular cell phone and the immunity of the particular hearing aid. The IEEE C63.19 standard [6] provides a rating scale which serves as a measure of the compatibility between cell phones and hearing aids. Consumers can look for this rating while purchasing a cell 13

14 phone or hearing aid. Appendix A has the results of a survey conducted at University of Florida which indicates that in order to avoid the EM interference, most aided hearing-impaired listeners prefer to remove their hearing aid in order to use the cell phone. Cell phone speech can sometimes be difficult to understand, because of the environmental noise, the reduced signal bandwidth ( Hz), the packet dropoffs and the vocoder artifacts. The environmental noise masks the speech while the reduced signal bandwidth and vocoder artifacts result in a loss in naturalness and intelligibility [7]. Hearing-Impaired listeners often find cell phones speech to be unintelligible. Modern hearing aids are extremely low power digital signal processor (DSP) based systems and provide gain and compression based on the individuals hearing loss through a process referred to as hearing-aid fitting. In addition, hearing aid DSPs also run feedback cancellation and noise reduction algorithms. In order to improve cell phone speech intelligibility for the hearing-impaired, powerful hearing enhancement algorithms can be run on the cell phones. This chapter will provide an introduction to sensorineural hearing-impairment and cell phone speech intelligibility. 1.1 Sensorineural Hearing Impairment Hearing impairment can be categorized both according to the type and the severity of the loss. The loss can be conductive or sensorineural. In conductive loss, the acoustical energy is attenuated uniformly by the outer and middle ears before reaching the cochlea. The signal processing solution for conductive loss is linear amplification. Sensorineural hearing loss occurs as a result of damage to the outer hair cells (OHC) and inner hair cells (IHC) of the cochlea [8]. Because of the nonlinear nature of this loss, simple linear amplification will not restore normal hearing. Hearing loss can be categorized based on the severity as mild (25 40 db HL), moderate (40 70 db HL), severe (70 95 db HL) and profound ( 95 db HL.) Hearing aids can help people with mild to severe hearing loss. The hearing aid algorithms attempt to imitate the OHCs acting to replace the damaged or 14

15 dead OHCs [9]. Cochlear implants have to be used if there is significant IHC loss as is the case with profound hearing loss. Appendix D provides a short description of how we hear and describes the roles of the IHCs and the OHCs Causes of Sensorineural Hearing Loss Hearing loss due to aging also known as presbycusis is the most common type of sensorineural hearing loss. It is a predominantly high frequency loss. Figure 1-1 shows the effects of aging on the thresholds of hearing. Presbycusis occurs due to wear and tear of the hair cells of the cochlea. Losses up to 60 db HL can be assumed to be caused by damage to the OHCs. For losses greater than 80 db HL, both the IHCs and OHCs have to be damaged. Sensorineural hearing loss caused due to exposure to loud sounds is called noise induced hearing loss (NIHL) [10]. Sounds at high intensities fatigue the hair cells of the cochlea and depending on the duration of exposure this may cause permanent damage. Portable audio devices like ipods can produce sound levels which can cause irreversible damage even when played for a couple of minutes [11]. Cell phones and bluetooth headsets also produce sound levels which can cause considerable damage. Recently there has been a lot of effort on the part of portable audio device manufacturers to educate the public on safe listening practices. Safe listening levels for music and speech have been estimated [11] using existing noise exposure standards [12], [13] Perceptual Measure of Sensorineural Hearing Loss Sensorineural hearing loss can be measured using several perceptual tests. The most commonly used one is the audiogram [14]. The audiogram measures the threshold of hearing in quiet. It is obtained by playing pure tones or narrow bands of noise, typically between Hz, at various intensity levels till it is just audible. The thresholds of hearing thus obtained are compared to the average normal hearing thresholds and the difference is reported in db HL (hearing level). People with perfect hearing will have an audiogram of 0 db HL. Normal hearing is defined as having all points 15

16 on an audiogram at or below 20 db HL. Figure 1-2 shows an audiogram of a person with a mild hearing loss measured using a Matlab GUI. On an average, a pure tone audiogram takes 5 minutes to be measured Characteristics of Sensorineural Hearing Loss Sensorineural hearing loss is characterized by four main effects: decreased audibility, decreased dynamic range or loudness recruitment, decreased frequency resolution and decreased temporal resolution [15], [8]. Decreased audibility. Sensorineural hearing loss results in the decreased audibility of high frequencies. This is because the basal OHCs which are worn out first are the ones closest to the oval window. Appendix D describes the mechanics behind how we hear and how hearing loss occurs. Figure 1-3 shows the hearing thresholds for a hearing-impaired and a normal-hearing listener and it can be seen that the hearing-impaired listener has higher thresholds of hearing especially at high frequencies. This decreased audibility results in low speech intelligibility because the consonants and the second, third formants of speech will not be audible. Since the loudness of speech is dominated by the low frequency components, the hearing-impaired listeners do not realize that they are missing out on part of the signal [16]. Even though cell phone speech is band limited to Hz, for 90% of hearing-impaired listeners the degree of hearing loss worsens from 500 Hz 4 khz [17] and this detrimentally affects the cell phone speech intelligibility. The audiogram provides a direct measure of the decreased audibility and is used in all hearing aid fitting algorithms. Decreased audibility can be compensated by providing a frequency dependent gain. Loudness recruitment. The uncomfortable listening level (UCL) is the level at which the sound is painful to listen to. For conductive hearing loss, the threshold of hearing and the UCL increase by the same amount. For sensorineural hearing loss only the threshold of hearing increases. This implies that sound levels which are uncomfortable for normal-hearing listeners are also uncomfortable for sensorineural hearing loss 16

17 listeners [18]. This results in a decreased dynamic range of speech and this phenomenon is called loudness recruitment. Loudness recruitment is measured using loudness scaling experiments. Figure 1-4 shows typical loudness growth curves measured using a six point loudness scale for a normal-hearing and a hearing-impaired listener. Decreased dynamic range can be compensated by providing compression. Decreased frequency resolution. Decreased frequency resolution [19] refers to the decrease in frequency sensitivity and frequency selectivity [20]. The OHCs increase the sensitivity of the cochlea to the particular frequency that the portion of the basilar membrane is tuned to. When the OHCs are damaged this sensitivity decreases. Frequency resolution can be measured using psychoacoustic tuning curves. Psychoacoustic curves are measured by playing an audible pure tone (probe) and varying the level of a narrow band of noise (masker) till the tone is barely audible. Figure 1-5 shows the psychoacoustic curves for a normal-hearing and hearing-impaired listener for a 4 khz tone with a 40 db masker. The tuning curve for the hearing-impaired listener is flat and broad (Figure 1-5) [21]. Because of this, the high energy, low frequency parts of speech will mask more of the weaker high frequency components. This is known as upward spread of masking [22]. Most often environmental noise is low frequency and because of upward spread of masking hearing-impaired listeners have a difficult time understanding speech in noise. Also, it has been shown that at high intensity levels even normal-hearing listeners have poor frequency resolution. This is because of saturation of the hair cells. Hearing-Impaired listeners always listen to high intensity sounds. This further worsens their frequency resolution [23]. Decreased frequency resolution can be compensated for to a certain extent by using sharp and narrow filter banks while processing the speech. Decreased temporal resolution. Temporal resolution refers to the ability to distinguish consecutively occurring sounds. Speech has a lot of temporal intensity variations and often the intense sounds can mask the weak sounds which occur immediately 17

18 after it. This effect is more pronounced for those with hearing impairment [24]. While listening to speech in an noisy environment, normal-hearing listeners extract most information from speech when the noise is low in magnitude. But because of reduced temporal resolution,these speech regions will be masked for the hearing impaired [25]. Temporal resolution is measured using psychoacoustic tuning curves. Figure 1-6 shows the psychoacoustic curves for the normal-hearing and hearing-impaired for a 4 khz probe tone with a 40 db masker. Decreased temporal resolution can be compensated by varying the gain so as to get normal masking threshold Modeling Sensorineural Hearing Loss Algorithms which simulate sensorineural hearing loss [26], [27], [28] help in the development and testing of compensatory techniques. For our research, we used a model based on both Moore [26] and Duchnowski [28]. The model simulates the decreased audibility and the loudness recruitment aspects of hearing loss. Spectrograms of cell phone speech at a normal conversational level for both normal-hearing and a typical mild to severe SNHL hearing loss of [ ] db HL are shown in Figure 1-7. The high frequency consonant information of speech is completely missing for the hearing-impaired and this results in low speech intelligibility (Figure 1-7b). The high energy low frequency part of speech is still present and makes the speech audible but unintelligible. Figure 1-8 is the setup used to model the hearing loss. The algorithm uses multiple filter bands and calculates the Hilbert transform for each filter band output. The envelope of the bandlimited speech obtained from the Hilbert transform is then raised and smoothed to obtain the effect of loudness recruitment. The modified envelope is then multiplied with the fine structure within the original envelope, to generate simulated lossy speech for that band. The outputs of all the filter bands are finally summed together to get the simulated lossy speech. The Matlab simulation used 30 filter banks with center frequencies equally spaced in mel frequency between 100 Hz to 8000 Hz. 18

19 1.2 Speech Intelligibility and Quality Speech intelligibility indicates the degree to which speech is understood by the listener [29] and speech quality indicates whether the speech meets the expectations of the listener. Subjective measures of evaluating speech intelligibility and quality are based on scores obtained via listening experiments. Objective measures of intelligibility and quality rely on signal-to-noise measurements and models of human speech perception Factors Influencing Speech Intelligibility and Quality Bandwidth. The frequency response of speech, both the shape and bandwidth, affects it s intelligibility [30]. Measurements show that the intelligibility of speech decreases with decreasing bandwidth. It is also important for the frequency response to be reasonably flat throughout it s range. For single words narrow band (NB) speech yields an accuracy of only 75%, while wide band (WB) speech results in a 97% accuracy [31]. This loss of intelligibility increases when multiple-word speech sounds are used to test intelligibility (Figure 1-9). Masking. Noise is any unwanted signal that interferes with speech and a decrease in the signal-to-noise ratio is the most common cause for a decrease in speech intelligibility. Masking is the phenomenon where the perception of speech is affected by the presence of noise [32]. Only noise which falls within the same critical bandwidth as speech can contribute to the masking of speech. Environmental noise is predominantly low frequency and is a strong masker which at high sound pressure levels can mask both the speech vowels and consonants [33], [34]. Distortion. Speech distortion is an unfavorable byproduct of certain signal processing techniques like coding, spectral subtraction, peak clipping and compression [35]. Independent multi-band operations change the temporal and spectral envelope of speech and this detrimentally affects the speech cues resulting in low SI [36]. To avoid audible artifacts, multi-band techniques are usually followed by some post-processing like envelope smoothening. 19

20 1.2.2 Speech Intelligibility Measures The most commonly used subjective measure of speech intelligibility is the hearing in noise test (HINT). The speech intelligibility index (SII) is the most commonly used objective measure of intelligibility. Subjective: Hearing in noise test. The hearing in noise test is a standard test of intelligibility commonly used in audiology [37]. Listeners are placed in a 65 dba constant noise environment and speech sentences at various signal levels are presented to them via headphones. The listener then has to repeat what he heard. The intensity of the next sentence is adaptively varied by ± 2 db or ± 4 db based on their response. It is stipulated that after 10 sentences, the final sentence intensity level converges to a level at which the listener recognizes 50% of the sentences correctly. This method of scoring intelligibility is called the reception threshold for sentences (RTS). A Matlab GUI was used to automate the test (Figure 1-10). The result of the HINT is a SNR value based on RTS. The lower the SNR value, the higher the speech intelligibility. Objective: Speech intelligibility index. The speech intelligibility index (SII) is the ANSI S standard for the objective measurement of speech understanding. Like the articulation index, it varies in value from 0 (speech is inaudible) to 1 (speech is audible and useful). The SII is not a direct measure of SI. But when the SII is used with empirically derived transfer functions, it can be translated to a speech recognition % correct score. SII and speech understanding have a monotonic relationship so higher the SII value the higher the speech understanding. The SII is calculated as shown in Equation 1 1. SII = n A i I i (1 1) i=1 In this formula, n refers to the number of frequency bands used which can vary from 6 octave bands to 21 critical bands. I i is the band importance function and A i refers to the band audibility, which ranges from 0 to 1 and indicates the proportion of speech cues that 20

21 are audible in a given frequency band. Details about I i and A i are available in the ANSI SII standard [38] Speech Quality Measures The mean opinion score (MOS) is the standard listening test used to measure speech quality. The perceptual evaluation of speech quality (PESQ) score is an ITU standard which provides an objective measure of speech quality. Subjective: Mean opinion score. The mean opinion score is a subjective listening test where sentences are played to the listener at a comfortable listening level. The listener then rates the quality of the sentences using a 5 point scale, as shown in Table 1-1. A Matlab GUI was created to automate the MOS test. Objective: Perceptual evaluation of speech quality. The perceptual evaluation of speech quality is the ITU-T P.862 [39] recommended standard for the objective measurement of the speech quality of narrow band systems. PESQ compares the original signal to a modified version of the same and predicts the perceived quality that would be given to the modified signal by subjects in a subjective listening test. The range of the PESQ score is -0.5 (extremely low quality) to 4.5 (excellent quality). 1.3 Cell Phone Speech Intelligibility The telephone bandwidth was restricted to Hz more than 60 years ago because of the limitations of the transducers then available. Even though the present day transducers operate on a wider frequency band, cell phone speech is still restricted to the narrow 3 khz bandwidth because of all the existing NB infrastructure. This reduced cell phone bandwidth makes it difficult to distinguish between consonants like f and s because the distinguishing F2 information lies above 3 khz. The elimination of frequencies below 250 Hz results in a loss in naturalness and comfort [7]. Figure 1-11 shows the frequency response of the Motorola i265 cell phone loudspeaker. It can be noted that response is not flat across frequencies and this further results in a loss in intelligibility. 21

22 Overall, the frequency response of the cell phone, both the bandwidth and the shape, results in speech with reduced quality and intelligibility. In addition, cell phone speech also has vocoder artifacts. Basically for any vocoder, the input speech is first divided into overlapping frames. A set of model parameters are then estimated for each frame, quantized and then transmitted. At the receiver, the decoder reconstructs the model parameters and uses them to generate a synthetic speech signal. The advanced multi-band excitation (AMBE) vocoder is commonly used with Motorola handsets [40]. Figure 1-12 shows the MOS speech quality ratings for the most commonly used vocoders. Depending on the data rate, AMBE has an average MOS score of 3.2 to 3.7. Clarity and the EAR foundation conducted a research study among a random group of 458 baby boomers between the age of [41]. 53% of the baby boomers reported having at least a mild loss and over 57% of baby boomers had trouble hearing on their cell phones. 40% of those who had problems using the cell phone said they would use the cell phone more often if they could hear the conversations more clearly while using it. Figure 1-13 lists the nature of the cell phone hearing problems. In order to better understand the cell phone needs of the hearing-impaired, focus groups and surveys on cell phone hearing were carried out at the University of Florida. A Cell phone hearing evaluation questionnaire, available from Appendix B, was created and handed out to 84 patients at the Shands speech and hearing clinic in Gainesville. Appendix A has the results from the questionnaire based survey and Appendix C discusses the main themes observed at the two focus groups which were conducted. The results from both these indicate the necessity of having algorithms run on the cell phone in order to enhance the hearing and improve speech intelligibility. 22

23 Table 1-1: Mean opinion score 5 point scale MOS Quality 1 Bad 2 Poor 3 Fair 4 Good 5 Excellent Figure 1-1. Effects of aging on hearing thresholds [42] 23

24 Figure 1-2. Matlab Audiogram GUI for a mild hearing loss Figure 1-3. Thresholds of hearing for the normal-hearing and hearing-impaired 24

25 Figure 1-4. Loudness growth curve for the normal-hearing and hearing-impaired Figure 1-5. Psychoacoustic tuning curve showing decreased frequency resolution 25

26 Figure 1-6. Psychoacoustic tuning curves for the normal-hearing and hearing-impaired 26

27 a Figure 1-7. Spectrograms of cell phone speech for A) Normal-Hearing B) Mild to Severe SNHL b 27

28 Figure 1-8. Simulated sensorineural hearing loss model Figure 1-9. Speech intelligibility measured using articulation index as a function of bandwidth [31] 28

29 Figure Hearing in Noise Test Matlab GUI Figure Speaker response for the Motorola i265 29

30 Figure MOS speech quality ratings for cell phone vocoders [40] Figure Nature of cell phone hearing problems [41] 30

31 CHAPTER 2 HEARING LOSS COMPENSATION ALGORITHMS Hearing aids have to be customized to each user s unique hearing loss. This is achieved by adjusting the gain and compression values of the hearing aid digital signal processor (DSP) using a prescriptive algorithm. This process is referred to as hearing aid fitting and the prescriptive algorithm used is called the hearing loss compensation algorithm or the hearing aid fitting algorithm. As mentioned in Chapter 1 and in Appendix D, mild to moderately severe sensorineural hearing loss (SNHL) is primarily caused by damage to the outer hair cells of the cochlea. So in effect, the hearing loss compensation algorithm has to imitate the outer hair cells (OHC) [9]. In order to run hearing enhancement algorithms on the cell phone for the hearing-impaired, the DSP of the phone has to be fit to the listener s hearing loss. This chapter will provide a brief review of the existing fitting algorithms and will detail the development of a new hearing loss compensation algorithm for cell phone speech, the recruitment based compensation (RBC) method. Speech processed by the new algorithm will be shown to have higher intelligibility and quality than the existing methods. 2.1 Review of Existing Hearing Loss Compensation Algorithms There are a number of existing hearing aid fitting algorithms [15] which vary in their rationale behind gain prescription. Some algorithms prescribe gain so that the speech is always at a most comfortable level (MCL) [43], others use loudness normalization or loudness equalization [44] as the rationale. Loudness normalization is a means of prescribing gain so as to make the loudness growth curve of the hearing-impaired the same as that for normal-hearing. Loudness equalization is based on the principle of equalizing the loudness information across frequencies. Intelligibility is assumed to be maximized when all the bands of speech are perceived to have the same loudness [45]. Figure 2-1 shows the basic classification of the hearing aid gain fitting algorithms. All these algorithms have been implemented in Matlab. 31

32 2.1.1 Threshold-Only Gain Prescription Procedures The threshold-only algorithms are simple linear prescription algorithms. They provide the same amount of gain for all input intensity levels based on the audiogram [46]. Just mirroring the audiogram would result in an ineffective fitting since the output will reach uncomfortable loud levels when the input signal is high in intensity. This is because of the decreased dynamic range aspect of SNHL. Since threshold-only algorithms do not include compression in the prescription, they should be followed by output limiting compression to prevent the sounds from getting too loud. Half-Gain rule. Lybarger in 1944 made the observation that while mirroring the audiogram resulted in an uncomfortable fit, providing half the gain of the audiogram resulted in speech being at the most comfortable level. The formula for fitting is given by Equation 2 1. IG f = 0.5 H f (2 1) Here IG f is the gain and H f is the frequency dependent hearing loss, Prescription of gain and output. The prescription of gain and output (POGO) [47] is a 1 2 gain rule with an attenuation term at the low frequencies. This is done to decrease the upward spread of masking. The formula for fitting is given by Equation 2 2. IG f = 0.5 H f + k f (2 2) Here IG f is the Gain, H f is the frequency dependent hearing loss, and k f is as shown in Table 2-1. POGO can be used for hearing losses up to 80 db HL. National acoustic lab-revised. The national acoustic lab (NAL) [48] of Australia published the national acoustic lab-revised (NAL-R) formula in It is the most popular of the threshold-only based fitting methods. The aim of the NAL-R procedure is to maximize listener intelligibility at the MCL by equalizing loudness. The NAL-R fitting formula is given by Equation 2 3. Table 2-2 indicates how the constant k f varies with 32

33 frequency. H 3F A = H H 1k + H 2k 3 X = 0.15 H 3F A IG f = X H f + k f (2 3) Suprathreshold Gain Prescription Procedures Suprathreshold fitting methods are those which prescribe both gain and compression using both the audiogram and the loudness growth curves. Unlike threshold-only based methods, suprathreshold methods vary the gain based on the input intensity. Fig6 fitting method. The Fig6 [49] procedure follows the loudness normalization rationale for medium and high level input signals. Fig6 prescribes gains for three different input intensity levels (40 db SPL, 65 db SPL and 95 db SPL) based on the audiogram and average loudness growth data. The three levels of speech represent the different levels of conversational speech with 40 db SPL representing soft speech, 65 db SPL representing conversational speech and 95 db SPL loud speech. Figure 2-2 shows the targets as prescribed by fig6. The 95 db SPL curve provides little gain for the low frequency sounds which are more intense than the high frequency sounds even for conversational level speech. The 65 db SPL and 40 db SPL curves provides more gain at the high frequencies. It can be seen that the amount of gain decreases when the input level increases. Independent hearing aid fitting forum method. The independent hearing aid fitting forum (IHAFF) [50] technique is based on loudness normalization and uses loudness scaling experiments instead of average loudness growth curves. The loudness scaling procedure used is the contour test and involves playing pulsed warble tones in ascending order from 5 db till the subject indicates that the stimulus is at the MCL. At each level the subject uses a 7 point rating scale to describe its loudness. The seven loudness categories for warble tones are condensed to three categories for speech 33

34 shown as the shaded horizontal bars in Figure 2-3. The visual input output locator (VIOLA) program then plots for each frequency an input-output curve with 2 compression thresholds and 2 compression ratios. An example input-output graph is shown in Figure 2-3. The diagonal line across the graph represents the 0 db gain. The distance between the IHAFF prescribed targets (the asterisks) and the diagonal line is the gain to amplify soft, average and loud input speech for that frequency. Desired sensation level fitting method. The desired sensation level (DSL) [51], [52] aims at making speech comfortably loud and audible. The gain for different hearing loss and frequency as used in the DSL 4.0 computer program is shown in Figure 2-4. The compression ratio prescribed by DSL is larger than that required to normalize loudness and is prescribed so as to fit the extended dynamic range from the normal-hearing threshold to the hearing-impaired UCL into the reduced dynamic range of the hearing impaired. DSL is the most popular suprathreshold fitting method. 2.2 Development of Recruitment Based Compensation Hearing loss compensation algorithms have to provide gains which vary with frequency and input levels [53]. For our novel method this is achieved by using filter banks as shown in Figure 2-5. Here S(n) is the incoming cell phone speech signal which is to be enhanced. Processing is carried out in the time domain using a frame-by-frame approach. S(n) is fed to a filter bank which has 14 bands with center frequencies equally spaced in mel frequency between Hz. The gain computation block uses the energy per band and the user s hearing thresholds to prescribe a gain and compression term for each band as per the RBC formula. The signals from each band are finally combined together to get the enhanced speech signal S e (c). The RBC gain block should be followed by a output limiting block which makes sure that the sounds never get painfully loud. The compression ratio and threshold for this stage are fixed at: 10:1 and 110 db SPL respectively. Loudness normalization is a method of prescribing gains so as to make the loudness growth curve for the hearing-impaired the same as that for the normal-hearing. Figure 2-6 shows the 34

35 loudness relationship for the normal-hearing and the hearing-impaired. The blue line shows the relationship between the sound levels judged to be at equal loudness by a normal listener. T n represents the normal threshold of audibility and serves as a reference for the typical impaired loudness growth which is shown in the red solid line. At the impaired threshold, T i, the perceived loudness is assumed to be equal to that of a signal at the normal threshold T n. At T c, the threshold of complete recruitment, the loudness for the impaired and the normal listener becomes the same. In 1959, Hallpike and Hood [54] showed that the range of recruitment, T c T i, is a fairly orderly function of the hearing loss, T i T n and is independent of frequency for unilateral hearing loss. Miskolczy-Fodor [55] further reported this behavior for presbycusis. Both these relationships are as shown in Figure 2-7. Let α be defined as the angle between the recruitment curve and the horizontal axis (Figure 2-6). The relation between α and hearing loss can be described by Equation 2 4. α = HL (2 4) Here hearing loss HL = T i T n. If R = T c T i is used to represent the recruitment range, the relation between the recruitment range and hearing loss can be described by Equation 2 5 R = HL tanα 1 In order to achieve loudness normalization, the algorithm amplifies the signal in (2 5) each channel such that the output level is related to the input level by the solid line. As the level is increased, the gain decreases until at T c the gain becomes one. From the audiogram, we can compute α and R and hence the gain factor per channel can be computed. This approach which is based on the frequency independent relationship between recruitment and hearing loss is called the recruitment based compensation (RBC) method. 35

36 The gain for each channel G db (w) is calculated as indicated by Equation set 2 6. G db (w) = P out (w) P in (w) P out (w) = m(w)p in (w) + b(w) m(w) = R(w) R(w) + HL(w) b(w) = (1 m(w))t c (w) T c (w) = R(w) + HL(w) + T n (w) R(w) = HL(w) tanα(w) 1 α = HL(w) (2 6) Here HL(w) is the hearing loss at the center frequency of each band which is obtained by the linear-interpolation of the audiogram. The RBC algorithm includes compression as part of the prescription and the compression ratio for each channel is given by CR(w) = 1. Compression is restricted to be within 1.1 and 3 for the hearing-impaired in order to m(w) prevent any artifacts While the existing algorithms which are also based on the concept of loudness normalization require the loudness growth curve for each frequency or the average loudness growth curves, all the RBC method requires is the audiogram of the hearing-impaired person. 2.3 Parameter Analysis of RBC Dynamic Constants of Compression Compression is used to reduce the dynamic range of speech so that it can fit within the reduced dynamic range of the hearing-impaired listener [56]. Compression can be carried out either in a single band on in multiple bands. In multi-band processing, each band usually has different compression characteristics and the degree of compression either increases or decreases with frequency. Typically 2 or 3 bands are used. Increasing the number of compression bands beyond 3 can result in audible distortion. Fitting algorithms 36

37 should always have output limiting compression to make sure that the sounds never get painfully loud. The attack and release times are the dynamic constants of compression and specify how quickly a compressor operates. If the effect of compression is instantaneous audible artifacts are produced because of the sudden change in levels. ANSI S3.22 defines the attack time as the time taken for the output to stabilize within 3 db of its final level after the input changes from 55 to 90 db SPL. The release time is defined as the time taken for the output to stabilize within 4 db of its final level after the input falls from 90 to 55 db SPL. Experimentally an attack time of 6 ms and a release time of 20 ms was found to be ideal. The implementation of the attack and release time constants compression is given by Equations 2 7. G ave (w) = β attack G i (w) + (1 β attack )G ave (w) G ave (w) = β release G i (w) + (1 β release )G ave (w) (2 7) Here G ave (w) is the average smoothed gain per band, G i (w) is the instantaneous gain per band, β attack and β release are the attack and release constants as defined in ANSI S3.22. Compression ratio is the inverse of the slope of the input-output curve. The compression ratio usually varies from 1.1:1 to 3:1. Compression threshold is the SPL above which compression kicks in. If loudness is to be normalized completely, compression should kick in from the threshold of normal-hearing which is 0 db SPL [57]. But useful speech sounds rarely occur below 30 db SPL. When the compression threshold is > 50 db SPL it is termed as high-threshold and when the compression threshold is < 50 db SPL it is termed as low-threshold. Wide dynamic range compression (WDRC) refers to systems which have low threshold. Figure 2-8 shows a typical WDRC characteristics with output limiting. Till the compression threshold of 50 db, the gain is linear. Compression is effective from after the threshold till the threshold of complete recruitment T c (80 db in this 37

38 example). The gain after T c is linear till it reaches the output limiting compressor s threshold Filter Bank Analysis The Matlab implementation of the RBC algorithm used 14 filter banks with center frequencies equally spaced in Mel frequency between 100 Hz to fs/2. The number of filter bank was chosen after listening experiments proved that 14 was the optimal number for maximum cell phone speech intelligibility for the hearing-impaired (Figure 2-9). In order to use RBC for the normal-hearing population a filter bank size of 5 was found to be optimal as a result of subjective listening tests. This is because normal-hearing listeners can hear the artifacts caused because of multi-band gain. Also, since typical normal-hearing population have little or no loudness recruitment effects, the compression parameters varied from 1.1 to 1.5. Compensation for reduced temporal and frequency resolution. The current algorithm overcomes the decreased audibility and the loudness recruitment aspects of sensorineural hearing loss by providing frequency and level dependent gain. By including 14 filter bands in Hz bandwidth we increase the frequency resolution. None of the existing fitting algorithms include processing to overcome the reduced resolution in time and frequency. Dead regions of the cochlea. The RBC algorithm limits the amount of gain per band based on the frequency and on the threshold of hearing for that band. If the band has a loss 80 db HL then the gain for that band is set to zero. This is done because speech intelligibility decreases when the listening levels are loud. Also, high frequency bands with high thresholds are penalized more than low frequency bands with high threshold. Severe loss such as 80 db HL usually occur due to damage to the IHCs and OHCs. An area of non-functioning IHCs is referred to as a dead region. The threshold equalizing noise (TEN) test [58] can be used clinically to detect dead regions of the cochlea. It is similar to measuring thresholds of hearing in noise and measures the 38

39 threshold for detecting a tone in a threshold-equalizing noise. The dead frequency regions are extrapolated to the filter band frequencies. Any band which lies in a dead region has it s gain set to zero. The first nearest neighbor frequency bands are also provided a gain lower than usual Real-Time Implementation Issues Audiogram on the phone. A Java midlet [59] was created to measure the thresholds of hearing using the cell phone. Since the cell phone is to be used as an assistive listening device for cell phone conversations and not a hearing aid, calibration is not a key issue. The midlet plays tones at different levels and the listener presses a key to indicate having heard the sound. The Motorola Roker E2 has 7 volume steps and by playing scaled tone wave files a volume range from 3 65 db was achieved. Figure 2-11 shows a depiction of how the audiogram on the phone would look. 2.4 Performance Analysis of the RBC Algorithm The performance of RBC was compared with linear amplification (LA), a simple high pass filter (HPF) [60], the DSL method, the HG method, the POGO method and the NAL-R method. The speech database unless otherwise mentioned is the standard HINT database. Cell phone speech was obtained by bandlimiting the speech to Hz and then passing it through an AMBE vocoder/decoder block to introduced the vocoder effects. Experimental Setup. The HINT and the MOS listening tests were run at the speech and hearing clinic, at the Gainesville Shands hospital in a sound treated booth using the Sennheiser HDA 200 head phones. 10 hearing-impaired patients with a pure tone average (500 Hz, 1 khz, 2 khz) of db HL were recruited. 10 normal-hearing were also recruited. Figure 2-12 shows the audiograms of all the hearing impaired subjects. Output limiting compression was provided for all the algorithms with a compression ratio of 10:1 and a compression threshold of 110 db. 39

40 2.4.1 Performance of Algorithm in Terms of Speech Quality Both the subjective mean opinion score (MOS) test and the objective perceptual evaluation of subjective quality (PESQ) scores were used to measure speech quality. PESQ speech quality measurement for hearing-impaired and normal hearing. To evaluate the performance of the new algorithm using PESQ the setup shown in Figure 2-13 was used. Typical mild to severe sensorineural hearing loss and typical normal-hearing were simulated using the Matlab hearing loss simulation block. The audiograms used were: [ ] db HL and [ ] db HL respectively. The unprocessed cell phone speech was passed through the hearing loss block to generate simulated loss speech. Speech preprocessed by the various fitting algorithms were passed through the hearing loss block to generate compensated speech. The objective PESQ scores were obtained using the original cell phone speech as the reference signal and comparing it to both the simulated loss speech and the compensated speech (Figure 2-14). PESQ is sensitive to distortion due to compression. The typical mild to severe SNHL modeled here would provide output levels at high frequencies which would turn on the output limiting compression. This results in low PESQ scores. If we compare with all the compression based systems RBC does the best followed by DSL and NAL-R. The scores also reveal that RBC outdid linear amplification and the other fitting algorithms for normal-hearing subjects with an average PESQ score greater than 4-Good. Spectrograms for hearing-impaired and normal-hearing. The spectrograms for the typical mild to severe SNHL simulated speech, linear-amplified speech and the speech compensated using the RBC method were obtained (Figure 2-15). The simulated hearing loss block was used to generate the speech (Figure 2-13). For the hearing-impaired, a lot of high frequency information is missing (Figure 2-15a). Linear amplification does not help because of the reduced dynamic range aspect of the 40

41 hearing loss (Figure 2-15b). Compression results in more high frequencies and this helps improve intelligibility (Figure 2-15c). The spectrograms for the typical normal-hearing simulated speech, linearly amplified speech and the speech compensated using the RBC method were also obtained (Figure 2-16). There is more high frequency information because of frequency dependent gain and this helps improve intelligibility. These results show that for both the normal-hearing and the hearing-impaired RBC has better speech quality and more useful frequencies than with just a linear gain which is what the cell phones volume control does. Subjective speech quality measurement for hearing-impaired and normal hearing: MOS. The MOS test provides subjective rating of speech quality in the absence of noise. For the unaided hearing-impaired listeners speech was played at 75 dba. For normal-hearing listeners speech was played at 65 dba. The Matlab MOS GUI was used to run this test. Figure 2-17 shows the average of the MOS scores for the hearing-impaired. For the hearing-impaired, RBC has an average MOS score greater than 4-Good. Figure 2-18 shows the average of the MOS scores for the normal-hearing. For the normal-hearing, RBC has an average MOS score greater than 4-Good Performance of Algorithm in terms of Speech Intelligibility Both the subjective HINT test and the objective SII scores were used to measure speech intelligibility. Objective speech intelligibility measurement for normal-hearing: SII. The speech intelligibility index (SII) was measured using the simulated hearing loss model for normal-hearing. Cell phone bandwidth speech both unprocessed and processed by the various fitting methods were passed through the simulated hearing loss block. The SII standard does not give a valid score for hearing-impaired speech. Figure 3-7 shows the variation of SII with SNR from -30 to

42 For the normal-hearing, RBC does marginally better than linear amplification for all SNR. An SII of 0.5 does not mean that speech is understandable 50% of the time. It means that about 50% of the speech cues are audible. For conversational speech an SII of 0.5 corresponds to about 100% intelligibility for normal-hearing listeners. Subjective speech intelligibility measurement for hearing-impaired and normal hearing: HINT. The Matlab HINT GUI was used in this test. The 10 hearing-impaired and normal-hearing subjects listened to both unprocessed HINT sentences and sentences processed by the different algorithms at various signal levels in the presence of a constant 65 dba noise. Figure 2-20 shows the averaged SNR for the 10 hearing-impaired subjects, with reference to the baseline (linear gain) for wide band speech. These scores show that RBC does the best followed by DSL and half-gain. NAL-R and half-gain. When compared to the linear gain technique RBC provides upto 15 db improvement in SNR. The difference between RBC and DSL for wideband speech is about 3 db. Figure 2-21 shows the averaged HINT results with narrow band cell phone speech input. These scores indicate that RBC does the best followed by NAL-R and half-gain. Half-Gain prescribes a higher gain than all the fitting methods being tested. For loud input levels, this will lead to a decrease in intelligibility but in the HINT the level of speech is reduced so the gain increment helps half-gain do better. The difference between RBC and linear gain is 15 db. The difference between RBC and DSL for cell phone speech is about 6 db. Figure 2-22 shows the averaged HINT results with narrow band cell phone speech input. These scores show that RBC does the best followed by NAL-R and HPF. The difference between RBC and linear gain is 6 db. The difference between RBC and DSL for cell phone speech is about 3 db. 42

43 2.5 Summary This chapter introduced a new hearing enhancement algorithm called recruitment based compensation. RBC is based on loudness normalization and is used to fit the cell phone to the user s hearing thresholds. The RBC stage is followed by an output limiting compressor to prevent damaging loud sound outputs. The performance of RBC was measured in terms of objective and subjective measures of speech intelligibility and quality. RBC was found to show consistent good performance. Table 2-1: The k f constant for POGO Freq k f Table 2-2: The k f constant for NAL Freq k f Figure 2-1. Classification of existing hearing aid fitting methods 43

44 Figure 2-2. Gains prescribed by the Fig6 method Figure 2-3. Input-Output curve at 2 khz obtained from the visual input output locator 44

45 Figure 2-4. DSL prescribed gain for different hearing loss Figure 2-5. Recruitment based compensation system 45

46 Normal HI Output Intensity (db) Tc Tn G w α HL w R Tn Ti Tc Input Intensity (db) Figure 2-6. Computation of gain based on loudness recruitment Recruitment range, R R = HL tan( hl) Hearing loss, HL (db) Figure 2-7. Estimated dependence of recruitment range on hearing loss 46

47 Figure 2-8. Compression input-output and gain curves 47

48 Ave SNR wrt baseline(db) None RBC:2 RBC:3 RBC:4 RBC:5 RBC:7 RBC:9 RBC:12 RBC:14 RBC:20 RBC:32 Algorithm:No of filter bands a Left ear Right ear HL (db) Frequency (Hz) Figure 2-9. Subjective HINT results for hearing-impaired A) Average HINT scores with varying filter bank size B) Average audiogram of the hearing-impaired listeners b 48

49 5 Excellent 4 Good 3 Fair 2 poor 1 Bad None HPF RBC:14NALR PG HG NALRP DSL Algorithm a HL (db) Frequency (Hz) b Figure Subjective MOS results A) Average MOS scores for the hearing-impaired B) Audiogram of the hearing-impaired listeners 49

50 Figure Audiogram on the phone Java midlet Figure Audiograms of all the hearing-impaired listeners 50

51 Figure Hearing loss simulation system Figure The PESQ objective speech quality score for normal-hearing and hearing-impaired 51

52 a b c Figure Spectrogram of hearing-impaired for A) Typical mild to severe SNHL B) Linear-Amplified speech C) RBC amplified speech 52

53 a b c Figure Spectrogram of normal-hearing for A) Typical normal-hearing B) Linear-Amplified speech C) RBC amplified speech 53

54 5 Excellent 4 Good 3 Fair 2 poor 1 Bad None HPF RBC:14NALR PG HG NALRP DSL Algorithm a HL (db) Frequency (Hz) b Figure Subjective MOS results A) Average MOS scores for the hearing-impaired B) Audiogram of the hearing-impaired listeners 54

55 5 Excellent 4 Good 3 Fair 2 poor 1 Bad None HPF RBC:14NALR PG HG NALRP DSL Algorithm a HL (db) Frequency (Hz) b Figure Subjective MOS results A) Average MOS scores for the normal-hearing B) Audiogram of the normal-hearing listeners 55

56 Figure Speech intelligibility index (SII) scores for normal-hearing as a function of SNR 56

57 5 Ave. SNR wrt baseline (db) None HPF RBC NALR PG HG NALRPDSL Algorithm a HL (db) Frequency (Hz) b Figure Subjective HINT results with wide band speech for the hearing-impaired A) Average HINT scores B) Audiogram of the hearing-impaired listeners 57

58 5 0 Ave. SNR wrt baseline(db) None HPF RBC:14 NALR PG HG NALRP DSL Algorithm a HL (db) Frequency (Hz) Figure Subjective HINT results with cell phone speech for the hearing-impaired A) Average HINT scores B) Audiogram of the hearing-impaired listeners b 58

59 2 1 Ave. SNR wrt baseline(db) None HPF RBC:14 NALR PG HG NALRP DSL Algorithm a HL (db) Frequency (Hz) Figure Subjective HINT results with cell phone speech for the normal-hearing A) Average HINT scores B) Audiogram of the normal-hearing listeners b 59

60 CHAPTER 3 NOISE ROBUST HEARING ENHANCEMENT ALGORITHMS Environmental noise detrimentally affects the intelligibility of speech [61] and this effect is more pronounced for people with hearing impairment. Speech is a highly redundant signal. In a moderately noisy environment, a normal-hearing listener will be able to understand what is being said even if some parts of the speech are masked by noise by virtue of the redundant nature of speech. Hearing-Impaired listeners deal with a less redundant speech signal because of the nature of their hearing loss [62]. This implies that even if the environmental noise masks a smart portion of the remaining speech, the intelligibility will be degraded significantly. The cochlea analyzes sound by means of a group of highly overlapping narrow band filters. These filters are called the critical bands and play an important role in noise masking. Only the noise which falls within the same critical band as speech can mask the speech. But the same noise will mask to a lesser extent, signals in higher frequency bands because of the highly overlapped structure of the critical filter bank. This effect is called the upward spread of masking and it increases with increase in noise intensity. This is also why low frequency sounds are better speech maskers. For the hearing-impaired the critical bands will be more broad and hence the upward spread of masking increases. This is why hearing-impaired listeners require a 9 db increase in SNR, when compared to normal-hearing listeners, in order to understand speech in noise [25]. This chapter will discuss the development of a noise robust recruitment based compensation (NR-RBC) algorithm. 3.1 Effects of Noise on Cell Phone Speech Cell phone noise can be classified based on where it originates as the transmitter side noise or the receiver side noise. The transmitter side environmental noise is often picked up along with the speech and is transmitted as part of the outgoing signal. The channel and vocoder produce some artifacts which are also transmitted. At the receiver end the incoming signal is processed in order to remove or reduce the noise before it is played. 60

61 The receiver side environmental noise can mask the incoming cell phone speech. Table 3-1 provides a list of cell phone noise sources and suggests possible ways to reduce it. 3.2 Development of Noise Robust Recruitment Based Compensation In order to reduce the effects of environmental noise masking at the listeners end, the hearing enhancement algorithms have to be tuned to the noise. Hearing aid fitting methods like DSL, NAL-R assume that a single frequency response is enough for speech intelligibility under all listening conditions. But recent studies show that different responses are desirable under different listening conditions. The factors that influence the best setting are the noise spectrum and the noise level. In addition to the frequency response, the best compression parameters also change with noise. The RBC algorithm uses the audiogram in quiet information to prescribe the gains. If masked thresholds of hearing are calculated then they can be used in the place of the thresholds in quiet in the RBC estimation method. The algorithm will then vary the gain and compression based on both the thresholds of hearing and the environmental noise. This modified algorithm is called the noise robust recruitment based compensation (NR-RBC) method. Figure 3-1 shows the block diagram of the procedure. Here S(n) is the incoming cell phone speech signal which is to be enhanced. Processing is carried out in the time domain using a frame-by-frame approach. S(n) is fed to a filter bank which has 18 bands with center frequencies and bandwidth as shown in Table 3-2. The environmental noise is picked up by the cell phone s calibrated microphone and is referred to as Y (n). The microphone also picks up the user s voice. In order to identify which frames contain noise, Y (n) passes through a voice activity detection block. The noise frames are then fed to a noise estimation block which provides an estimate of the noise for each octave-band. This octave-band noise estimate is used to compute the noise masked thresholds. The gain computation block uses the energy per band, the noise estimate and the user s hearing thresholds to prescribe a gain and compression term for each band as per the NR-RBC 61

62 formula. The signals from each band are finally combined together to get the enhanced speech signal S e (c) Single Microphone Noise Estimation The cell phones microphone signal is referred to as Y (n) (Figure 3-1). During pauses in the conversation Y (n) picks up the environmental noise. Using a voice activity detection system the frames can be monitored for noise and speech. If a noise flag is set then the noise power estimate is then updated. Using the single microphone system an estimate of the environmental noise N(w) at the listeners end has to be calculated. This will be done during pauses in the conversation. Techniques like Minima Controlled Recursive Averaging (MCRA) [63] method and others [64] are available for robust estimation of noise. We used a simple voice activity detector based on spectral distance Calculating the Noise Masking Threshold The noise masking threshold is calculated for the incoming cell phone speech. If the environmental noise lies below the noise masking threshold then the gain prescription formula is the same as for RBC. The noise masking threshold can be obtained by modeling the frequency selectivity and masking properties of the cochlea [65], [66]. As a first step a critical band analysis has to be carried out in order to know which speech bands of the incoming cell phone signal will be affected by the environmental noise. This can be achieved by passing the noise through filter bank structure similar to the one used in RBC 2-5. While this will lead to an accurate analysis, it will be computationally inefficient to implement on the phone. A way to work around this is to group together the FFT bins based on the critical band center frequencies and bandwidth. Table 3-2 lists the critical band number, center frequency, bandwidth and the FFT bin details for a bin size of 256 and a sampling frequency of 8000 Hz. Critical band analysis is carried out on the power spectrum of the signal over the FFT bins which correspond to each critical band. 18 critical bands cover the cell phone frequency range up to 4000 Hz. Since the critical bands are highly overlapped structures 62

63 the critical band power signal has to be convolved with a spreading function in order to estimate the effects of masking across critical bands. The spreading function proposed by Schroeder [67] is given by Equation log 10 T i = (i ) 17.5(1 + (i ) 2 ) 1/2 (3 1) where, i is the critical band number and T i is the spreading function. The next step involves the calculation of the noise masking threshold, given by Equation??, which includes an offset term O i which is specified in Table?? T i = 10 log 10C i (O i /10) (3 2) The noise spread threshold has to be converted back to the Bark or critical band domain. This is done by renormalization. The bark thresholds are compared to the individuals absolute thresholds of hearing HL(w) also in the bark scale. The noise masking threshold T (w) for any critical band which has a noise threshold lower than the absolute threshold is changed to the absolute threshold Derivation of Noise Robust Recruitment Based Compensation If the environmental noise is below noise masking thresholds for the speech, then the usual RBC formula is used. If the noise is greater than the threshold of hearing then the new gain can be calculated as follow: The gain for each channel G db (w) is calculated by Equation set 3 3. HL n (w) = T (w) + m(w) = (N(w) T (w), when T (w) < N(w) (3 3) 2 G db (w) = P out (w) P in (w) P out (w) = m(w)p in (w) + b(w) R(w) R(w) + HL n (w) b(w) = (1 m(w))t c(w) T c (w) = R(w) + HL n (w) + T n (w) R(w) = HL n(w) tanα(w) 1 63

64 α = HL n (w) 3.3 Performance Analysis of the NR-RBC Algorithm The performance of NR-RBC was compared with RBC and the other existing algorithms in terms of subjective and objective measures of speech intelligibility and quality. Experimental setup. The HINT and the MOS listening tests were run at the Shands speech and hearing clinic in a sound treated booth using a modified headset. The modified device was shaped to look like a cell phone. 10 hearing-impaired patients with a pure tone average (500 Hz, 1 khz, 2 khz) of db HL and 10 normal-hearing listeners were recruited Performance of Algorithm in Terms of Speech Quality Both the subjective mean opinion score (MOS) test and the objective perceptual evaluation of subjective quality (PESQ) scores were used to measure speech quality. PESQ speech quality measurement for hearing-impaired and normal hearing. To evaluate the performance of the new algorithm using PESQ the setup shown in Figure 2-13 was used. A typical mild to severe sensorineural hearing loss and a typical normal-hearing was simulated using the Matlab hearing loss simulation block. The audiograms used were: [ ] db HL and [ ] db HL. The unprocessed cell phone speech was passed through the hearing loss block to generate speech with simulated loss. Then speech preprocessed by the various fitting algorithms were passed through the hearing loss block to generate compensated speech. Among the HA fitting algorithms which include compression, NR-RBC has the maximum PESQ score. For the normal-hearing NR-RBC had a slightly lower PESQ score this could be due artifacts. In order to understand this the spectrograms of simulated normal-hearing speech were calculated. 64

65 Spectrograms for hearing-impaired and normal hearing. Figure 3-3 shows the spectrograms for the hearing-impaired speech, linearly amplified speech and the speech compensated using the NR-RBC method. These results show that for the hearing-impaired NR-RBC has better speech quality and intelligibility than with just a linear gain which is what the cell phones volume control does. Figure 3-4 shows the spectrograms for the normal-hearing speech, linearly amplified speech and the speech compensated using the NR-RBC method. These results also show that for the normal-hearing NR-RBC has better speech quality and intelligibility than with just a linear gain. Subjective speech quality measurement: MOS. The MOS test provides subjective rating of speech quality in the absence of noise. For the unaided hearing-impaired listeners speech was played at 75 dba. For the normal hearing listeners speech was played at 65 dba. The room where the MOS test was conducted had a noise floor of in a room with a noise floor of 46 dba. The Matlab MOS GUI was used to run this test. Figure 3-5 shows the average of the MOS scores for the hearing-impaired. For the hearing-impaired, NR-RBC has an average MOS score greater than 4-Good. Figure 3-6 shows the average of the MOS scores for the normal-hearing. For the normal-hearing, NR-RBC has an average MOS score greater than 4-Good Performance of Algorithm in terms of Speech Intelligibility Both the subjective hearing in noise test (HINT) and the objective speech intelligibility index (SII) scores were used to measure speech intelligibility. Objective speech intelligibility measurement: SII. The SII was measured using the simulated hearing loss model for normal-hearing. Cell phone bandwidth speech both unprocessed and processed by the various fitting methods were passed through the simulated hearing loss block. The sentences were then normalized and passed through the SII measurement block while varying the SNR. It can be seen that NR-RBC has the highest SII score at all SNRs. 65

66 Subjective speech intelligibility measurement: HINT. The Matlab HINT GUI was used in this test. From the HINT tests in the Chapter 2 we know that RBC and DSL show the best performance. The performance of NR-RBC was compared to RBC and DSL with varying filter sizes. Figure 3-8 shows the averaged SNR for the 10 hearing-impaired subjects, with reference to the baseline (linear gain). It is clear that NR-RBC outperforms RBC and DSL and has best performance at N=14 (Figure 3-8). The difference between NR-RBC and the best DSL is about 20 db. Figure 3-9 shows the averaged SNR for the 10 normal-hearing subjects, with reference to the baseline (linear gain). It is clear that NR-RBC outperforms RBC and DSL and has best performance at N=8.The difference between NR-RBC and the best DSL is about 13 db. 3.4 Summary Environmental noise degrades the speech intelligibility for both normal-hearing and hearing-impaired listeners though the degree of degradation is more for the hearing-impaired. The recruitment based compensation algorithm was modified to include the noise term by introducing the noise masked threshold. This leads to the noise robust recruitment based compensation method (NR-RBC). This creates a fitting system which varies the gains based on both the environment and the hearing thresholds. The noise robust recruitment based compensation method was found to have good performance in terms of speech intelligibility and quality for both the hearing-impaired and the normal hearing. 66

67 Table 3-1: Sources of cell phone noise and noise-reduction methods Cell phone noise Ways to reduce noise Receiver side environment noise To prevent it from being transmitted: Beamforming and AGC To help you hear better: RBC algorithm (Automatically adjust gain) To help you hear better: Occlude contra-lateral ear Transmitted environment noise To reduce its effects: Spectral subtraction Vocoder and channel noise To reduce its effects: Spectral subtraction Table 3-2: Critical bands and FFT bins Critical band details FFT details Noise masking details N = 256, f s = 8kHz threshold Critical band Center freq Bandwidth FFT critical band Offset term number (Hz) (Hz) range (Hz) (db)

68 68 Figure 3-1. Noise robust recruitment based compensation (NR-RBC) system

69 69 Figure 3-2. The PESQ objective speech quality score for various HA fitting algorithms

70 a b c Figure 3-3. Spectrogram in noise of hearing-impaired for A) Typical mild to severe SNHL B) Linear-Amplified speech C) NR-RBC amplified speech 70

71 a b c Figure 3-4. Spectrogram in noise of normal-hearing for A) Typical simulated normal-hearing B) Linear-Amplified speech C) NR-RBC amplified speech 71

72 5 Excellent 4 Good 3 Fair 2 poor 1 Bad None RBC:14 NR RBC:14 DSL Algorithm a HL (db) Frequency (Hz) b Figure 3-5. Subjective MOS results A) Average MOS scores for the hearing-impaired B) Audiogram of the hearing-impaired listeners 72

73 5 Excellent 4 Good 3 Fair 2 poor 1 Bad None RBC:14 NR RBC:14 DSL Algorithm a HL (db) Frequency (Hz) b Figure 3-6. Subjective MOS results A) Average MOS scores for the normal-hearing B) Audiogram of the normal-hearing listeners 73

74 Figure 3-7. The SII scores for simulated normal-hearing as a function of SNR Figure 3-8. The HINT scores for RBC (A1), NR-RBC (A2) and DSL (A3) with variation of filter size for hearing-impaired 74

75 Figure 3-9. The HINT scores for RBC (A1), NR-RBC (A2) and DSL (A3) with variation of filter size for normal-hearing 75

76 CHAPTER 4 ACCLIMATIZATION MODELING FOR THE AIDED HEARING IMPAIRED The auditory cortex undergoes physiological and anatomical changes over a period of time when presented with altered auditory signal inputs. In the case of a person with mild to severe SNHL, the altered auditory signal will have little or no high frequencies. Moore [68] provided a review of studies showing evidence of plasticity in the auditory system of the adult brain. Because of this brain plasticity, it takes time for the aided hearing-impaired listeners to fully use the high-frequency information that they were previously not used to hearing. This is known as the acclimatization effect. The time period for acclimatization is defined as the period between when the hearing loss was noticed and when the hearing aid was fitted. Acclimatization is more pronounced for new hearing aid users and affects the hearing aid fitting procedure. For a first time hearing aid user, the audiologist will first measure the amount of loss, discuss the various hearing aid options (styles, binaural or monoaural) and then choose a make and model of a hearing-aid. Ear mold measurements of the patient are then made. The hearing aid will arrive after 2-3 weeks and the patient will be fit with the hearing aid. Fitting is the procedure by which the hearing aid parameters are tuned for the patient s hearing loss. Usually, each hearing aid is accompanied by a CD with the company s proprietary fitting software which also allows selecting certain established fitting procedures like DSL and NAL. As long as the initial fitting parameters do not cause any discomfort, they will not be modified during the first visit. During the follow up visits, the audiologist will fine-tune the parameters based on verbal feedback from the patient. The verbal feedback is descriptive and indicates how certain sounds are now being perceived with the hearing-aid. The patient is not asked to rate the sounds on any scale. The fine-tuning process is repeated over multiple visits until the hearing aid user is satisfied with a particular fitting. The follow up visits are usually apart by a couple of weeks. 76

77 It is hypothesized that initially patients choose the amplification characteristics which gives them the greatest gain at frequencies where they have the least loss because they are used to hearing sounds at those frequencies. After a month, they then prefer high frequency emphasis. It is also possible for patients to get acclimatized to a particular hearing aid fitting. In such cases, the initial fitting parameters should be the optimum ones. A compromise is to provide patients with a response that slowly varies over time from the response they prefer to the response that is best for them. This will enable them to gradually get used to a new response without subjecting them to a sound quality that they are not happy with. 4.1 Development of the Fitting Satisfaction Scale In the analysis of multi-session fitting data the final fitting parameters are considered to be optimum since they provide the best sound experience for the patients. But this definition of optimality is ambiguous since it can mean either best sound quality or best speech intelligibility. This is because while manually adjusting the HA fitting parameters audiologists rely on verbal patient feedback which is ambiguous. A better and more structured approach would be to use a fitting satisfaction scale at each session to evaluate the fitting. The three main psychoacoustic phenomena associated with SNHL, elevated threshold, loudness recruitment, and frequency blurring, lower the speech intelligibility for hearing-impaired listeners by degrading the speech cues. Our previous research in this area [69] has shown that SI based fitting methodologies show better performance in noise and other real world scenarios. Hence it is better to use a rating scale, as shown in Table 4-1, based on SI where 1 is low intelligibility and 5 is high intelligibility. It is assumed that the listeners are not trained listeners, that the speech stimuli are sentences and that the listeners have no prior cues about the speech stimuli. 77

78 4.2 Hearing Aid Fitting Data Hearing Aid Fitting Data Collection Multi-session fitting data was collected from patients using Phonak hearing aids (HA). The Phonak HAs targeted were the Savia (30 patients), Claro (20 patients), Extra (7 patients), Valeo (4 patients), Perseo (3 patients) and Eleva (2 patients). All the patients were fit binaurally. The left and right ear HAs were viewed as two separate inputs since the audiograms for both the ears were different. The fitting software used with the Claro used the desired sensation level (DSL) fitting procedure while the rest used the national acoustic lab nonlinear (NAL-N1) procedure. Each HA users had 3-8 follow-up sessions which were separated by a maximum of 3 months. The HA users used their perception of speech to judge improvements between sessions and did not provide any rating. Table 4-2 shows some of the fitting parameters provided by the listed Phonak HAs. All HAs allow frequency based fine-tuning of the gain parameters and the maximum power output (MPO) values at one or several input signal levels. Some hearing aids also allow modification of the compression parameters (CR, TK, TK knee) across frequency Multi-Session Hearing Aid Fitting Data Analysis Since most of the Phonal HAs use either NAL-NL1 or DSL[i/o], the fitting data of one of each type was compared. National acoustic lab nonlinear (NAL-NL1) prescribes gain which is similar to the NAL-R procedure but it also includes compression unlike the NAL-R. NAL-NL1 prescribes less gain at the low frequencies compared to the other suprathreshold methods and this is more evident at low input levels. DSL[i/o] and RBC prescribe gain at lows in order to normalize loudness. Figure 4-1 shows the variation of the 50 db and 80 db gains for the Claro HA, as prescribed by DSL, between the first and last fitting. For the Claro, more people tend to prefer having lesser gain at the final-fitting than at the initial-fitting. This goes against our hypothesis that patients will slowly increase the HF gain. This could be explained by 78

79 the fact that DSL[i/o] has been known to over prescribe gain at 500Hz,2kHz and 4kHz at both input levels [15]. Figure 4-2 shows the variation of the 40 db and 80 db gains for the Savia HA, as prescribed by NAL-NL1, between the first and last fitting. For the Savia more people prefer having higher gain at the final-fitting than at the initial-fitting. This could be explained by the acclimatization process. Figures 4-3 and 4-4 show the variation of the compression parameters for the Claro and Savia HA. It can be seen that more people tend to not change TK for both Claro and Savia. For Claro HAs, the change in compression ratio does not follow any conclusive trend while for Savia the compression ratio seems to increase at the final fitting. This could be explained by the fact that NAL-NL1 prescribes lesser CR at all frequencies compared to the other suprathreshold methods [15]. In order to study the variation of the all the parameters for each HA, the trend in change for each parameter was first averaged across the frequency. The maximum trend across this average was then picked up and plotted for each HA in Figures 4-5 to 4-9. For the Claro which was fit by DSL, from Figure 4-6it appears that the gain parameters decrease at the final fitting while the TK, MPO parameters remain the same and the CR decrease at the final fitting. For the NAL-NL1 based HAs it appears that the frequency averaged gain parameters increase at the final fitting. For Savia (Figure 4-5), Extra (Figure 4-7), and Valeo (Figure 4-8) the frequency averaged compression parameters remain the same across the fitting stages. For Eleva (Figure 4-9) and Perseo (Figure 4-10) the frequency averaged compression parameters decrease across the fitting stages. 4.3 Modeling the Acclimatization Effect A neural network (NN) can be used to model the variations from the initial fitting to the final fitting (best response). The use of NNs in the area of hearing aids is not a new thought. NNs have been used with HAs in the task of noise reduction [70] and to 79

80 help select the HA model based on the patient information [71]. J.M. Kates [72] studied the feasibility of using NNs to derive HA prescriptive procedures. Kates concluded that the factors which affected the accuracy of a NN based fitting method were the training database size, the variability of the patient s responses and the variability of the hearing loss. Gao [73] proposed a new hearing prosthetics similar to the one proposed by Kates which was based on NNs and fuzzy logic. Rather than using NNs to replace existing fitting algorithms, we propose to use the NNs along with the existing fitting algorithms to model the acclimatization effect. The NN was trained on the multi-session fitting data using supervised learning. The input was the initial fitting data and the desired was the final fitting data. In addition to the fitting data, some HA user specific parameters such as patient s age, degree of hearing loss (HL) and the number of years with HL were used as inputs during training. Figure 4-11 shows the structure of the NN used in the acclimatization modeling setup. The NN used to model acclimatization was the multi-layer perceptron [74]. It had 7 sigmoidal input nodes and 7 linear output nodes. The number of hidden nodes was varied until the lowest error was obtained and was found to be 4. Training was carried out using the Levenberg-Marquardt [75] method with an initial global learning rate (LR) of Cross validation was used to stop the training in order to prevent over-training. The NN was trained on the Savia data which had the most number of data points. 4.4 Performance Analysis of Model The NN was trained using validation to prevent over-training. There were 60 multi-session data vectors for the Savia. The multi-session data was divided into 40 training sets, 10 validation sets and 10 test sets. 10 iterations with random initial weights, training, testing and validation tests were run. A separate NN was used to model each parameter The MSE error between the predicted and the target was used as our figure of merit and comparison. 80

81 Figures 4-12to 4-17 show the results of training for the Savia HA. In the figures, the red curve shows the MSE between the initial fitting values and the desired or optimal values. From the figures, it can be observed that the gains predicted by the NN are closer to the optimal values than by just using the initial values. There exists some error between the predicted and the optimal values especially at the low frequencies and this might be resolved by increasing the training database. From the figures, it can be seen that the neural network succeeds in modeling the trend with a certain amount of error. The error can be brought down by increasing the training database. The MSE of the optimum setting is always less than that with the initial setting. 4.5 Summary Acclimatization occurs due to the plasticity of the auditory cortex. Fitting the HA with the close to optimal solution at the first visit will both reduce the number of follow up fitting sessions and result in a more optimum fit since the brain will get readjusted to hearing the right frequencies from the first session. A neural network was trained to model the variation of the change in parameters across fitting sessions for the Phonak Savia. The results show a low test on new data MSE for all the parameters. A fitting satisfaction scale based on SI was also proposed which will further help with the acclimatization modeling by providing a statistic label to each session data. Table 4-1: Speech intelligibility based fitting satisfaction scale Speech intelligibility Score Speech is never intelligible 1 Speech is rarely intelligible 2 Speech is sometimes intelligible 3 Speech is usually intelligible 4 Speech is always intelligible 5 81

82 Table 4-2: Phonak hearing aid fitting parameters Hearing Aids Fitting Input Frequency (khz) No of No of parameters level(db) parameters Patients Savia Gain 40 [ ] 6 30 Gain 60 [ ] 6 30 Gain 80 [ ] 6 30 CR - [ ] 6 30 TK - [ ] 6 30 MPO - [ ] 6 30 Claro Gain 50 [ ] 7 20 Gain 80 [ ] 7 20 CR - [ ] 5 20 TK Knee - [0.50 3] 2 20 TK MPO Extra Gain 40 [ ] 6 7 Gain 60 [ ] 6 7 Gain 80 [ ] 6 7 CR - [ ] 6 7 TK - [ ] 6 7 MPO - [ ] 6 7 Valeo Gain 50 [ ] 6 4 MPO Perseo Gain 50 [ ] 6 3 Gain 80 [ ] 6 3 TK - [ ] 6 3 MPO - [ ]

83 a b Figure 4-1. Comparison of change from initial to final stage for Claro parameter A) 50 db Gain B) 80 db Gain 83

84 a Figure 4-2. Comparison of change from initial to final stage for Savia parameter A) 50 db Gain B) 80 db Gain b 84

85 a Figure 4-3. Comparison of change from initial to final stage for Claro parameter A) TK and B) CR b 85

86 a Figure 4-4. Comparison of change from initial to final stage for Savia parameter A) TK and B) CR b 86

87 20 18 Final<Init Final=Init Final>Init No of Patients L40:6 L60:6 L80:6 R40:6 R60:6 R80:6 RTk:6 LTk:6 RMpo:6 LMpo:6 RCr:6 LCr:6 Fitting Parameter:No of Frequencies Figure 4-5. Phonak Savia maximum trend in fitting parameter variation averaged across frequencies Final<Init Final=Init Final>Init No of Patients L50:7 L80:7 R50:7 R80:7 RTk:2 LTk:2 RMpo:1 LMpo:1 RCr:5 LCr:5 RTKTh:1LTKTh:1 Fitting Parameter:No of Frequencies Figure 4-6. Phonak Claro maximum trend in fitting parameter variation averaged across frequencies 87

88 6 Final<Init Final=Init Final>Init 5 4 No of Patients L40:6 L60:6 L80:6 R40:6 R60:6 R80:6 RTk:6 LTk:6 RMpo:6LMpo:6 RCr:6 LCr:6 Fitting Parameter:No of Frequencies Figure 4-7. Phonak Extra maximum trend in fitting parameter variation averaged across frequencies 88

89 4 3.5 Final<Init Final=Init Final>Init No of Patients L50:6 R50:6 RMpo:1 LMpo:1 Fitting Parameter:No of Frequencies Figure 4-8. Phonak Valeo maximum trend in fitting parameter variation averaged across frequencies 89

90 2 1.8 Final<Init Final=Init Final>Init No of Patients L40:6 L60:6 L80:6 R40:6 R60:6 R80:6 RTk:6 LTk:6 RMpo:6 LMpo:6 RCr:6 LCr:6 Fitting Parameter:No of Frequencies Figure 4-9. Phonak Eleva maximum trend in fitting parameter variation averaged across frequencies 90

91 3 Final<Init Final=Init Final>Init No of Patients L50:6 L80:6 R50:6 R80:6 RTk:6 LTk:6 RMpo:1 LMpo:1 Fitting Parameter:No of Frequencies Figure Phonak Perseo maximum trend in fitting parameter variation averaged across frequencies 91

92 Figure Structure of the MLP used to model multi-session fitting trends 92

93 a b Figure Phonak Savia neural network modeling results for 40dB gain A) test on train data and B)test on new data 93

94 a b Figure Phonak Savia neural network modeling results for 60dB gain A) test on train data and B)test on new data 94

95 a b Figure Phonak Savia neural network modeling results for 80dB gain A) test on train data and B)test on new data 95

96 a Figure Phonak Savia neural network modeling results for CR parameter A) test on train data and B)test on new data b 96

97 a Figure Phonak Savia neural network modeling results for TK parameter A) test on train data and B)test on new data b 97

98 a b Figure Phonak Savia neural network modeling results for MPO parameter A) test on train data and B)test on new data 98

99 CHAPTER 5 CONCLUSIONS The US census bureau states that 50 million people, nearly one-fifth of the US population, are in some way disabled. Among this 28 million people are hearing-impaired. With suitable technological assistance these men and women (notably the aging baby boomers) may prolong their independence and reduce their need for specialized care. Their quality of life will be improved. While no product can be designed so that every single person in the world can use it, the intent is to maximize the potential of each device. This dissertation proposes using the cell phone as an assistive listening device. This will enable the 20 million hearing-impaired people who do not use hearing aids understand cell phone speech better. This will also help normal-hearing listeners especially in noise situations. Sensorineural hearing loss (SNHL) is mostly caused by damage to the outer hair cells (OHC). So in particular, the hearing loss compensation algorithm has to replace the damaged outer hair cells. A novel algorithm based on the frequency independent relationship between hearing loss and recruitment was developed. This recruitment based compensation (RBC) algorithm prescribes both the gains and compression parameters. RBC shows a 15 db improvement in speech intelligibility when compared to the baseline algorithm (linear gain) for the hearing-impaired. For the normal hearing the SNR difference between RBC and linear gain is 6 db. By providing frequency dependent gain and compression the decreased audibility and decreased dynamic range aspects of SNHL are overcome. This is carried out by processing the speech signal in 14 filter banks. The filter banks have their centers equally spaced in mel frequency and is so designed keeping the auditory processing of the OHCs in mind. The OHCs lose their abilities to increase the sensitivity of the cochlea for frequencies to which the affected part of the cochlea is tuned. Psychoacoustically, this shows up as flatter tuning curves. Because of this, noise has a greater masking effect for 99

100 hearing-impaired people. The noise robust recruitment based compensation (NR-RBC) algorithm was developed to improve the performance of RBC in noise. NR-RBC uses a noise estimate to calculate the noise masked threshold for speech which is used in the gain prescription. NR-RBC outperforms RBC and DSL in terms of speech intelligibility. The difference between NR-RBC and DSL for the hearing-impaired is 20 db and for the normal-hearing is 13 db. Both RBC and NR-RBC have a MOS speech quality rating of Good. The auditory cortex undergoes physiological and anatomical changes in the presence of altered auditory input. Because of this brain plasticity, it takes some time for the hearing-impaired to learn to fully use the high-frequency information that they were previously not used to hearing. This is known as the acclimatization effect. Multiple-session data for a number of Phonak hearing aids was collected and analyzed. Neural network were used to model the acclimatization effect in hearing aid fitting. Low mean square error (MSE) for test on new data was obtained. Contribution summary. Three novel hearing enhancement algorithms were developed as part of this multi-disciplinary research which was the result of a proposal written by us. A cell phone hearing evaluation questionnaire was created to understand the needs of the hearing-impaired. Focus group meetings were conducted and video testimonials were obtained to further narrow down on the main problems faced by the hearing-impaired. Higher speech intelligibility was found to be a main requirement. Three novel algorithms all based on the rationale of maximizing speech intelligibility were created. RBC is aimed towards helping both normal-hearing and unaided hearing-impaired listeners understand cell phone speech better. NR-RBC is a noise robust technique which enhances the hearing in noise. Acclimatization modeling is proposed to improve the quality of the initial fit for the aided hearing-impaired listener. The hearing enhancement algorithms were tested extensively in terms of objective and subjective measures of speech quality (SQ) and intelligibility (SI) on both normal-hearing and hearing-impaired subjects 100

101 and were shown to have good performance in terms of both quality and intelligibility. A FFT based filter bank approach to implement both RBC and NR-RBC was also proposed which leads to an easy realtime implementation. Matlab code was written for the hearing loss simulation, for the audiogram GUI, for the three new algorithms, for all the popular hearing aid fitting methods mentioned in this dissertation, for the automated subjective SI and SQ tests. 101

102 APPENDIX A SURVEY OF HEARING-IMPAIRED CELL PHONE USERS This chapter discusses the results of the cell phone hearing evaluation survey. 84 hearing-impaired participants answered 20 hearing aid and cell phone related questions and provided other relevant demographic information. The questionnaire used in the survey is available in Appendix B. A.1 Participants 84 subjects with hearing loss were recruited from four different University of Florida clinics during patient visits and voluntarily completed the surveys in the fall of 2004 and spring of All participants were previous or current cell phone users. The participants hearing impairment ranged from mild to profound and the distribution is shown in Figure A-1. Hearing aid usage experience varied among the subjects. Subjects ranged from 23 to 89 years of age with a mean age of years old. Fifty-two males and 32 females participated in the survey. A.2 Results Review of the completed surveys showed some questions were left unanswered by some participants. Therefore, the survey data posted below indicates the number of participants who responded to each question. A.2.1 Cell Phone Usage Of the 84 participants, 62 were experienced hearing aid users and the remaining 22 had not tried hearing aids. Five of the 62 hearing aid users were fit unilaterally and the other 57 were fit binaurally. Only 4 of the 62 hearing aid users wore completely in canals (CICs) and the remaining hearing aid styles were almost evenly split between behind the ears (BTEs) and in the ears (ITEs). Sixty-one cell phone users indicated their estimated time spent on their cell phone and the results showed that 8 individuals use their cell phone just for emergencies, 7 reported over 400 minutes of use each month, while the remaining 45 reported less than 400 minutes on the cell phone each month. The average 102

103 age of respondents who reported frequent cell phone usage was 61.6 years while those who used their phones less frequently was 70.0 years, and finally those who reported they do not use their cell phone frequently was 75.6 years. Sixty respondents identified the style of cell phone they use, and 55% indicated that their cell phone was a flip phone while the other 45% used a candy bar style phone. Two cell phone users used neckloops. A.2.2 Electromagnetic Interference 14 of 35 hearing aid users indicated that their cell phone created EM interference in their hearing aid and 8 of these 14 reported that the noise was so severe that it had prevented them from using their cell phone. Two participants thought that the noise might have occurred and were not sure about it, while two others experienced interference from the cell phone backlight. Analysis of variance indicated that 78% of respondents who reported noise in their hearing aid used candy bar style cell phones and the other 22% were flip phone users. The two individuals who used neckloops expressed concern that the neckloop would not be compatible with other phones. Other individuals commented that they were not interested in buying any additional equipment to make their hearing aids compatible with their cell phone. A.2.3 Cell Phone Speech and Ringer Level One in three of the hearing-impaired cell phone users reported trouble hearing their cell phone ring. 31.8% of cell phone users who did not use hearing aids had trouble hearing the ringer. 31 of 54 cell phone users desired louder cell phone speech levels and 1/3rd of those desiring more volume were non-hearing aid users. The need for higher cell phone output level was also shared by persons with hearing loss ranging from mild to profound. Figure A-2 indicates the self-reported understanding of cell phone speech in quiet and in noise for both aided and unaided hearing-impaired participants. A.2.4 Summary and Conclusions The results of the survey indicate that a large percentage of the hearing-impaired have difficulty using the cell phone effectively because of obstacles such as electromagnetic 103

104 (EM) interference and insufficient cell phone signal and ringer volume. ANSI standard C63.19 [6] indicates how hearing aid and cell phone manufacturers should measure the EM interference. The measurements are translated into M-ratings in which the higher ratings indicate a lower likelihood of interference. Handsets that receive a hearing aid compatibility rating of M3 or higher have met or surpassed FCC requirement. The FCC has required that cell phone companies have 50% of their handsets meet a minimum ANSI rating of M3 or better by February 18, Figure A-1. Degree of hearing impairment among survey participants 104

105 Figure A-2. Degree of hearing impairment for survey participants 105

106 APPENDIX B CELL PHONE HEARING EVALUATION QUESTIONNAIRE 1. Is your hearing loss: (a) Mild- A little difficulty hearing speech (b) Moderate- More difficulty hearing speech (c) Severe- A lot of difficulty hearing speech (d) Profound- So bad that hearing aids may not help 2. Do you use Hearing aids? (a) Yes (b) No 3. Your Left Ear Hearing aid is: (a) None (b) ITE- In The Ear (c) BTE-Behind the Ear (d) CIC-Completely In Canal (e) Other 4. Your Right Ear Hearing aid is: (a) None (b) ITE- In The Ear (c) BTE-Behind the Ear (d) CIC-Completely In Canal (e) Other 5. What is the make and model of your Hearing aid? 6. Check whichever is true: (a) I can understand speech over the telephone with my hearing aid 106

107 (b) I can understand speech over the telephone in noisy environments with my hearing aid (c) I can understand speech over the telephone without my hearing aid 7. Does your Hearing aid have a telecoil? (A feature available on many hearing aids is the telecoil or t-switch or t-coil which aids in hearing telephone conversations.) (a) Yes (b) No 8. If your Hearing aid has a telecoil, do you use your telecoil with your cell phone? (a) Yes (b) No (c) I don t use cell phones 9. Do you frequently use cell phones? (a) Yes (b) No 10. Check whichever is true: What has been your general experience with cell phones? (a) I can understand speech on the cell phone (b) I can understand speech on the cell phones in noisy environments (c) I have trouble hearing my cell phone ring (d) I cannot understand speech on the cell phones because: If you don t use cell phones regularly jump to question 17 else continue 11. What is the make and model of the cell phone you use? (Example Make= Motorola Model=v300) 12. Is it a Flip phone? (a) Yes 107

108 (b) No 13. Do you use a neckloop for cell phone conversations? (A neckloop is a necklace-size loop of wire worn around the neck of someone who has a hearing aid with a telecoil.) (a) Yes (b) No 14. Which Cell phone Network provider do you use? (a) Cingular (b) Tmobile (c) Verizon (d) Sprint (e) Other 15. How many minutes per month do you talk on the cell phone? (a) Just for emergencies (b) <200 (c) (d) 400+ (e) Don t know 16. Check whichever is true: (a) I wish my cell phone would be louder (b) My cell phone create noise in my Hearing aid (c) This noise prevents me from using my cell phone (d) A person talking near me on a cell phone produces noise in my Hearing aid 17. Does your cell phone backlight cause noise in your Hearing aid? (a) Yes (b) No (c) Not noticed 108

109 18. Would you be interested in a combination Hearing aid and cell phone? (a) Yes (b) No 19. Is there enough information available online to help you choose the cell phone right for you? (a) Yes (b) No 20. What information regarding cell phones and hearing aids would you like to see available? 109

110 APPENDIX C ANALYSIS OF THE FOCUS GROUP DISCUSSIONS In order to better understand the needs of the hearing-impaired population two focus groups with hearing-impaired participants were conducted. This chapter provides a synopsis of the main themes which were observed at the two focus group. C.1 Participants The two focus groups were held on 06/18/2004 and 09/24/2004 at the University of Florida speech and hearing clinic at Shands hospital in Gainesville. Focus group one lasted approximately two hours and was attended by 3 hearing-impaired subjects. All of them were hearing-aids users and and had used cell phones. Focus group two was attended by 10 hearing-impaired subjects. All of them owned hearing aids and had used cell phones.the hearing-impaired participants were informed that they were at the meeting to give their opinions, answer questions, ask questions, nominate topics and generate ideas. The sessions were audio taped and later transcribed. C.2 Focus Group Main Themes C.2.1 Aided Cell Phone Listening Problems The placement of the microphones in the behind the ear (BTE) hearing aids makes it tricky to couple the cell phone loudspeaker output to the hearing aid. There was the worry that, in trying to find the sweet spot for the BTE hearing aid (HA), the phone would be placed at such an odd angle that the person on the other end would not be able to hear you speak. Some people find it easier to remove their HAs to use the cell phone but do not like doing so. The HA volume had to be at maximum to hear the cell phone conversation and this caused feedback for the in the ear (ITE) hearing aids. Louder cell phone output was requested by all. Some participants reported electromagnetic (EM) interference between the cell phone and the HA. There was also trouble hearing the phone ring. 110

111 C.2.2 Ideal Hearing Aid Compatible Cell Phone The ideal cell phone would be one which would not have any feedback or placement problems. It would have volume control which would allow for louder levels and which would offer frequency based adjustments so as to match their unique losses. The phone would be a flip-phone style which was found to have less EM interference with the HA. It would have a hands-free option where it could be directly coupled to the hearing-aid. Control over the ring tone volume level and frequency would also be provided. C.2.3 Comments on a Cell Phone Assistive Listening Device There was the thought that one could wear one normal hearing aid on one ear and a handsfree cell phone assistive listening device (ALD) on the other ear.the idea of taking a hearing test on the cell phone was favored by all. There was some worry about whether the phone would be too loud and the option of fitting the phone at the audiology clinic was mentioned. There was the thought that fitting a cell phone to meet a hearing loss and also using the HA might provide the increase in volume required to hear speech especially for those with high losses. A hands-free solution, similar to a bluetooth headset, was appreciated. In the absence of a hands free solution, there was worry over taking out the HA in order to use the phone. 111

112 APPENDIX D PHYSIOLOGY OF HEARING The human ear, the organ of hearing and balance, is the best example of an engineering masterpiece. It enables us to hear sounds ranging from 20 Hz-20 khz with a dynamic range of db. Anatomically, the human ear can be divided into three parts: the outer ear (pinna, auditory canal), the middle ear (ossicles, eardrum, oval window) and the inner ear (cochlea, semicircular canals). Figure D-1, shows the internal structure of the ear. The human pinna is symmetric, points forward and has a curved structure. It focuses sound pressure waves into the auditory canal. The structure of the pinna aids in sound localization. Horizontal localization is made possible because of inter-aural time and intensity differences while vertical localization is made possible because of the frequency shaping of the sound by the curves of the pinna. The auditory canal which is around 2.7 cm in length acts as a 1 4 wave closed tube resonator and boosts the 2-5 khz region by 15 db. The broad resonance peak is because the closed end of the auditory canal is the pliant ear drum or tympanic membrane. The middle ear consists of the eardrum, the ossicles (malleus, incus and stapes) and the oval window. The ossicles translate the sound pressure wave to vibrations in the cochlea. They provide impedance matching since the acoustic impedance of the fluid in the cochlea is about 4000 times that of air. The ossicles provide amplification by lever action (3x) and by terms of area amplification (15x). The ossicles also help block very loud low frequency sounds by means of the stapedius reflex. The stapes transmits vibrations to the oval window on the outside of the cochlea. This moves the fluid in the cochlea which forms a traveling wave, with a peak at one location along the length of the cochlea. Conductive hearing loss occurs when sound is not conducted efficiently to the cochlea through the ossicles. The cochlea is intact for conductive hearing loss. 112

113 The cochlea is the body s microphone. It converts the mechanical movement into electrical action potentials which are then carried to the brain through the auditory nerve. The cochlea is a snail-shell like structure and contains three fluid filled canals. One of them the organ of corti has a lining called the basilar membrane (BM). Hair cells are arranged in four rows along the entire length of the cochlear coil (Figure D-2). Three rows consist of outer hair cells (OHCs) and one row consists of inner hair cells (IHCs). Each hair cell has hundreds of tiny stereocilia. The stereocilia of the OHCs are embedded in the tectorial membrane. The traveling wave bends the IHC s stereocilia and this produces action potentials. The afferent IHCs transmit signals to the brain via the auditory nerve. The efferent OHCs receive neural input from the brain which influences its motility as part of the cochlea s mechanical pre-amplifier. The OHCs help the IHCs sense soft sounds by sharpening the peak of the traveling wave. Based on feedback from the brain, the OHCs mechanically shrink pulling down the tectorial membrane. Once the IHCs cilia brushes against the tectorial membrane, action potentials are generated. In addition to amplifying soft sounds, OHCs also sharpen the peak of the traveling wave resulting in high frequency resolution. Sensorineural hearing loss occurs because of damage to the hair cells. Hearing loss due to aging or presbycusis is a type of sensorineural hearing loss and occurs due to wear and tear of the hair cells. A hearing loss up to 60 db HL can be considered to be because of OHCs and anything higher than 80 db HL is because of both IHC and OHC damage. Figure D-3, shows the hair cells for a person with normal-hearing and a person with severe hearing loss. Damage to the OHCs indicates a lower frequency resolution and the inability to hear soft sounds while damage to the IHCs indicates that the sound information is not being sent to the brain. There is a tonotopic mapping along the length of the BM (Figure D-4). Each part of the BM has a characteristic frequency of maximum vibration which depends on its relative position. At the base of the cochlea (near the oval window), the BM is stiff and thin and 113

114 hence more responsive to high frequencies. The apex of the cochlea is wide and floppy and more responsive to low frequencies. Each IHC has about 10 auditory nerve (AN) fibers. The AN fibers also have a tonotopic mapping and encode steady state sounds and onsets. At the stimulus onset, the AN firing increases rapidly. For constant stimulus, the firing rate decreases exponentially. The AN pathway passes from the cochlea to the brainstem and then upwards to the auditory processing centers of the temporal lobes of the brain which decode the neural signal and provides us with the sensation of sound. Figure D-1. Structure of the human ear [76] 114

115 Figure D-2. Hair cells of the Organ of corti [77] a b Figure D-3. Electron micrograph of the organ of corti for A) Normal-hearing B) Severe hearing loss [78] 115

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