An Improved Time Domain Pitch Detection Algorithm for Pathological Voice

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1 Amercan Journal of Appled Scences 9 (1): , 2012 ISSN Scence Publcatons An Improved Tme Doman Ptch Detecton Algorthm for Pathologcal Voce Mohd Redzuan Jamaludn, Shekh Hussan Shakh Salleh, Tan Tan Swee, Kartn Ahmad, Ahmad Kamarul Arff Ibrahm and Kamarulafzam Ismal Centre for Bomedcal Engneerng, Unversty Technology Malaysa Skuda, Malaysa Abstract: Problem statement: The present study proposes a new ptch detecton algorthm whch could potentally be used to detect ptch for dsordered or pathologcal voces. One of the parameters requred for dysphona dagnoss s ptch and ths prompted the development of a new and relable ptch detecton algorthm capable of accurately detect ptch n dsordered voces. Approach: The proposed method apples a technque where the frame sze of the half wave rectfed autocorrelaton s adjusted to a smaller frame after two potental ptch canddates are dentfed wthn the prelmnary frame. Results: The method s compared to PRAAT s standard autocorrelaton and the result shows a sgnfcant mprovement n detectng ptch for pathologcal voces. Concluson: The proposed method s more relable way to detect ptch, ether n low or hgh ptched voce wthout adjustng the wndow sze, fxng the ptch canddate search range and predefnng threshold lke most of the standard autocorrelaton do. Key words: Ptch Detecton Algorthm (PDA), dysphona, autocorrelaton, Merged Normalzed Forward Backward Correlaton (MNFBC), pathologcal voces, Hlbert-Huang Transform (HHT), tme doman, mean error, Auto Correlaton Functon (ACF) INTRODUCTION Vocal cords wthn the laryngeal structure vbrate due to ar passng through them durng voced speech (Swee et al., 2010). Durng voced phonaton ptch s produced and the fundamental frequency, F 0 and ts recprocal known as ptch perod, T 0 can be calculated (Amado and Flho, 2008; Kotnk et al., 2009; Manfred et al., 2000). Vocal hyperfuncton, vocal abuse and msuse, or unhealthy socal habts such as smokng and alcohol consumpton may over tme, cause physcal changes to the laryngeal structure and lead to voce changes such as loss of power, changes n ptch and reducton n voce range (Hadjtodorov and Mtev, 2002; Tmmermans et al., 2002; Godno-Llorente et al., 2006; De Bodt et al., 2007). Cycle-to-cycle ptch perod perturbaton (also known as jtter) s usually one of the parameters used to measure voce qualty. In order to obtan an accurate ptch perod for each cycle of voced phonaton, the Ptch Detecton Algorthm (PDA) needs to be able to perform equally well n pathologcal voces (Manfred et al., 2000; Jang et al., 2007; Schoentgen, 2003). The detecton of ptch s dffcult due to the followng reasons: The nonstatonarty and quasperodcty of the speech sgnal as well as the nteracton between the glottal exctaton and the vocal tract (Ahmad and Spanas, 1999; Chen and Wang, 2001; Rabner et al., 1976) False ptch estmates can also be caused by nose and sgnal dstorton that occur n real envronments and errors n vocng decson (Ca and Lu, 1997; Tabrkan et al., 2004; Chomphan, 2011) For dysphonc voces, there are sgnfcant perturbaton of ampltude and frequency n the voced sgnal, presence of subharmonc and aperodc components of hgh ntensty and also nfluence of voced sgnal formant structure (Mtev and Hadjtorov, 2003) Many Ptch Detecton Algorthms (PDA) have been developed and yet the results are not adequately relable n detectng ptch n pathologcal voces (Mtev and Hadjtorov, 2003). Correspondng Author: Mohd Redzuan Jamaludn, Centre for Bomedcal Engneerng Unversty Technology Malaysa Skuda, Malaysa 93

2 There are several known types of tme doman based PDA. The most promnent one s the Auto Correlaton Functon (ACF). The followng shows the general equaton of the ACF (Abdullah-Al-Mamun et al., 2009; De Chevegne and Kawahara, 2002; Quater, 2002; Lahat et al., 1987; Moman, 2009): + N 1 Rx = s[n]s[n + l] (1) Where: R x = The autocorrelaton value s[n] = The nput speech sgnal at sample = The frst sample nsde a frame n N = The frame sze l = The lag or tme dsplacement that ranges from zero to the number of sample per frame mnus one The lag value that produces maxmum peak wll be chosen as the ptch perod. Accordng to De Chevegne and Kawahara (2002), another type of autocorrelaton equaton s as the followng: + N l 1 Rx = s[n]s[n + l] (2) (c) Fg. 1: Acoustc waveform of an /a/ utterance; The correspondng ACF accordng to Eq. 1; (c) The correspondng ACF accordng to Eq. 2 Ths study amed to propose a newly developed tme doman PDA wth mproved relablty n detectng dsordered ptch. The PDA was tested on the KayPENTAX Elemetrcs database for the vowel /a/ from 50 normal voces and 100 pathologcal voces randomly selected. The results were compared wth the datasheet provded by KayPENTAX Elemetrcs for the accuracy test. The performance of the proposed PDA was also compared wth the well-known and publcly avalable PRAAT toolkt (Kotnk et al., 2009). 94 Fgure 1a s a frame of speech waveform of the vowel /a/. The equaton ncludes the lag, l to be subtracted from n to produce ACF as shown n Fg. 1c whle Eq. 1 produces ACF n Fg. 1b and 2. ACF produced by Eq. 2 degraded as the l value ncreases by tme. Fgure 1b-c show the ACF of the acoustc waveform whch were normalzed and half-wave rectfed from Fg. 1a. It can be seen from Fg. 1b that there are two domnant ACF peaks and these are termed as ptch canddates. The frst peak s at lag = 146 and the second peak les at lag = 292. Usually, to choose the best ptch to be defned as the ptch perod of the frame, a rule must be set whereby the range of choosng the best ptch should not be near to zero lag and should not exceed certan value of lag. Ths rule reflects the lmt for human ptch range whch s Hz (Mtev and Hadjtorov, 2003). Most of the exstng commercalzed software such as PRAAT and Computerzed Speech Laboratory by Kay Elemetrcs requre the users to specfy ther own fundamental frequency range of nterest n order for the algorthm to work effcently. Some lterature also proposed the use of ACF threshold so that only peaks that exceed ths predetermned

3 threshold wll be notfed as ptch canddates (Mtev and Hadjtorov, 2003). But these rules lack flexblty. If the range s poorly specfed, the algorthm wll take the wrong lag as ptch perod. If the range s not specfed at all, the autocorrelaton wll not be able to accurately detect low ptched voce as reported by Samad et al. (2000). The threshold rule wll also be napproprate for Eq. 1-2 snce some of the voces ACF do not even exceed 0.5 or more. Another method s called the Average Magntude Dfference Functon (AMDF) (Manfred et al., 2000). The general equaton for AMDF, R y s as follows Eq. 3 (Quater, 2002; Chong and Shh-Chen, 1977): + N 1 R y = s[n] s[n + l] (3) Unlke ACF whch selects the maxmum peak as the ptch canddate, AMDF tends to search the mnmum peak as the ptch canddate. Manfred et al. (2000) proposed the modfed AMDF where the frst valley found to be less than the threshold s set to be the ptch perod of the frame. Ths approach also has ts weakness smlar to the ACF whereby some harmoncs and nose effects can also produce AMDF values that falls below ths threshold. From these basc tme doman PDA s, many researchers have modfed these algorthms so that t wll work more effcently to obtan ptch. One of the nterestng approaches was Merged Normalzed Forward Backward Correlaton (MNFBC) whch bascally used the same concept of autocorrelaton but nstead of usng autocorrelaton, t uses MNFBC whch s to be nose robust (Kotnk et al., 2009). Plus the method of fndng the exact ptch perod was by mplementng vterb search to the MNFBC. The vterb searches for three largest value of the MNFBC as the ptch canddates per voced frame. But the vterb search ntroduces hgh dependency on current frame s ptch value wth the prevous frame s ptch value and t wll not be able to work effcently wth dysphonc voces snce cycle-to-cycle ptch perod can vary extremely from each other. False perod estmaton can also occur when the MNFBC value s larger at ptch canddates other than the true ptch perod. Huang and Pan (2006) and Donato et al. (1999) proposed Hlbert-Huang Transform (HHT) for PDA whch was developed to consder the non-lnearty characterstcs of speech sgnal. It was proven to produce better accuracy of ptch detected but the Am. J. Appled Sc., 9 (1): , computatonal requrements are also ncrease (Kotnk et al., 2009). Jang et al. (2007) Expermented several PDA s to be mplemented on pathologcal voces and the result showed that ACF was the most credble PDA to detect pathologcal voce. Mtev and Hadjtorov (2003) presented that wth a lttle modfcaton to ACF, t can be an accurate PDA to be appled to pathologcal voces. But the method stll depends on a threshold whch they used was 0.5. Some of the pathologcal voces have fewer ACF than 0.5 even at the ptch perod. These fndngs ndcate that ACF tme doman based PDA can stll be able to detect ptch n dysphonc voces wth hgh accuracy. From all of the nformaton gven above, ths study s proposng ACF wth modfcaton and wth less computatonal cost for ptch detecton n dysphonc voces wthout usng a predetermned threshold and can also automatcally set the ptch searchng range unlke most of the commercal software where the users themselves need to set the searchng range. MATERIALS AND METHODS The proposed algorthm for PDA s based on tme doman approach conssts of the modfed ACF. The procedures are as the followng. Step (1): Intalzaton: Let t = 1 be the ntal pont of the speech sgnal. The frame sze used for the algorthm s two tmes maxmum ptch perod, MAX_PER. MAX_PER s the lowest ptch that human can produce whch s 60 Hz of voced speech sgnal so that at least wthn ths frame sze, two best ACF peaks can be chosen as ptch canddates. Step (2): Compute autocorrelaton: The autocorrelaton equaton used s Eq. 2. The equaton wll produce ACF or R x wth s equals to t, l ranges from zero to 2*MAX_PER - 1 and N s equals to 2*MAX_PER. Step (3): Half-wave rectfcaton and normalzaton: The ACF s then normalzed and half-wave rectfed so that the values for consderaton are normalzed and postve. Ths technque was ntroduced by Kotnk et al. (2009) usng the followng procedures: R x s calculated usng Eq. 2. R o and R t are found usng the followng formulae Eq. 4 and 5:

4 Ro + N 1 s[n]s[n] = (4) + N 1 Rt = s[n + l]s[n + l] (5) where smlar to (2), s[n] s the nput speech sgnal at sample n, s the frst sample nsde a frame, N s the frame sze and l s the lag. Lag l ranges from zero untl N-1. Then the normalzaton of R x s done by usng the followng formula Eq. 6 and 7: Fg. 2: AMDF of Fg. 1a s waveform R = + N 1 l s[n]s[n + l] + N 1 + N 1 ( s[n]s[n])( s[n + l]s[n + l]) (6) Or: R = R R o x R t (7) Then R s half-wave rectfed by settng all the negatve values to zero Step (4): Mark all possble canddates: All the peaks of the ACF are then marked as possble ptch canddates, T (). Fgure 3a shows one frame of ACF of the vowel /a/ and Fg. 3b s the marked peaks, T. The algorthm has consdered several condtons for the system to work effcently after every T are beng recognzed: If there s no T, or = 0, then the ptch for that frame s set to 0 and the frame moves to the next frame as much as 2*MAX_PER. If T exsts, go to step (5) Fg. 3: ACF of an /a/ utterance. Marked ACF peaks 96 Step (5): Fnd two best canddates: The algorthm wll then fnd the best two canddates by sortng the R x at every T from the largest value to the lowest value along wth ther T as shown n Table 1. From the rearranged canddates, the best two canddates are found by frstly use the followng Eq. 8 to fnd the dfference between a par of T : dff T (1) T (j) = (8) where, j = 2, 3, 4,, j total and the best two canddates are chosen based on the followng condton Eq. 9: dff (2 * MAX _ PER) / 8 (9)

5 Table 1: Marked peaks before rearrangement and after rearrangement of ACF shown n Fg. 3 Rearranged accordng to descendng order Marked canddates of ACF values j ACF (R) Perod (T 1) ACF (R) Perod (T 1) The frst T par that acheves ths condton wll be kept as b 1 and b 2 for the next step. The value (2*MAX_PER)/8 was obtaned expermentally as values lower or hgher than ths wll degrade the performance of the proposed PDA whch s to accurately detect the ptch. Fgure 4 shows the two ptch canddates whch have been marked. Step (6): Create new frame: Once the two canddates are selected, the sze of the new frame wll be calculated as the followng Eq : c = b1 b2 (10) new _ frame = MIN _ PER (11) new _ framef = c + (c / 4) (12) Where: new_frame = The ntal pont of the new frame whle new_framef = The fnal pont of the new frame Instead of searchng the ptch wthn 1 untl MAX_PER-1 range or wthn a predefned range as most of the ACF does n the lterature, ths study ntroduces the new searchng range whch wll be from new_frame untl new_framef. Wth the new frame ntroduced, the largest R x (T ) value that les wthn that range wll be chosen and ts correspondng T s consdered as the ptch perod, T 0. Fgure 5 shows the new frame or the new regon to search the T 0 and the T 0 s marked wth blue lne. Fg. 4: The marks ndcate the best two canddates chosen Step (7): Proceed to the next frame: Snce the frame sze used mght be consstng of two or more ptches, the startng pont of the new frame s found accordng to the followng Eq. 13: tnew = tprev + T0 (13) Fg. 5: The red marks ndcate the range between new_frame and new_framef. The blue lne marks the ptch perod 97 Where: t new = The new frame s startng pont, t prev = The prevous frame s startng pont and = The ptch perod found from the prevous frame T 0 Ths way, every ptch perod or every ptch epoch can be located accurately as shown n Fg. 6. The experment was conducted to test the accuracy and the effectveness of the proposed PDA on normal voces and pathologcal voces.

6 Fg. 6: The marks ndcate the start and the end of a ptch perod One of the experments conducted was applyng the PDA on /a/ utterances from the KayPENTAX Elemetrcs voce database conssts of 50 normal voces and 100 functonal and organc voce dsorders. The parameters that were used to be compared wth the datasheet of results gven along wth the database were the average fundamental Frequency (F0), hghest Fundamental frequency (Fh), lowest fundamental frequency (Flo) and Standard devaton of the fundamental frequency (STD). The reference values were consdered as the true values. The error percentage was calculated by usng the followng Eq. 14: = (14) pproposedpda preference err(%) 100% preference Where: p proposedpda p reference = The value of each parameter obtan by usng the proposed PDA = The value of each parameter gven by the reference The results were also compared wth the wellknown and publcly avalable PRAAT toolkt where the PRAAT autocorrelaton (PRAAT_ac) was chosen because the proposed algorthm s a modfed autocorrelaton (Kotnk et al., 2009). RESULTS Table 2 shows the errors of each parameter produced by usng the proposed PDA whle Table 3 presented the errors of each parameter produced when PRAAT_ac was used. 98 Fg. 7: Error percentage for mean fundamental frequency of PRAAT (autocorrelaton) for normal voce. Error percentage for mean fundamental frequency of proposed PDA for normal voce Fgure 7-10 shows the comparson of the error produced by usng PRAAT_ac and the proposed PDA. The observaton shows that the PRAAT_ac works well for normal voces as to compare wth the proposed algorthm. However, the error dfferences between PRAAT_ac and the proposed PDA are only at a very small scale. Table 2-3 show the mean of the errors for each voce sample and each parameter by usng two dfferent PDA s. Accordng to the results from Table 2-3, PRAAT_ac produces more error for the pathologcal voce than the proposed PDA. To summarze the result obtaned by usng the proposed PDA and PRAAT_ac, every parameter was averaged to get the mean error for each voce sample. For the proposed PDA t has been found that for 49 normal voces, the mean error was less than 20% and one voce was classfed to be havng more than 20% error, 13 pathologcal voces had more than 20% average error whle another 87 pathologcal voces had less than 20% mean error. These data are presented n Table 4.

7 Table 2: Errors n percentage produced by usng the proposed ptch detecton algorthm Voce pattern Normal Pathologcal Mean fo Hghest fo Lowest fo Standard devaton Am. J. Appled Sc., 9 (1): , 2012 Table 3: Errors n percentage produced by usng PRAAT (autocorrelaton) Voce pattern Normal Pathologcal Mean fo Hghest fo Lowest fo Standard devaton Table 4: Classfcaton of voces accordng to the mean error of each voce sample usng the proposed ptch detecton algorthm Voce pattern Error < 20% Error > 20% Normal 49 1 Pathologcal Table 5: Classfcaton of voces accordng to the mean error of each voce sample usng the praat_ac Voce pattern Error < 20% Error > 20% Normal 49 1 Pathologcal Fg. 9: Error percentage for lowest fundamental frequency of PRAAT (autocorrelaton) for normal voce. Error percentage for lowest fundamental frequency of proposed algorthm for normal voce Fg. 8: Error percentage for hghest fundamental frequency of PRAAT (autocorrelaton) for normal voce; Error percentage for hghest fundamental frequency of proposed PDA for normal voce Whle n Table 5, smlar to the proposed PDA, there were 49 voces dentfed to be havng less than 20% error and only one voce was put n the more than 20% error category. For pathologcal voce, there are 15 voces were havng mean error of more than 20% and another 85 voces had less than 20% mean error. 99 Fg. 10: Error percentage for standard devaton of the fundamental frequency of PRAAT (autocorrelaton) for normal voce. Error percentage for standard devaton of the fundamental frequency of proposed algorthm for normal voce DISCUSSION Even though t was observed that PRAAT_ac works better for normal voces, Fgure 11 untl Fg. 14 presented that t works poorly for pathologcal voces whle the error produced by usng the proposed algorthm s smaller for pathologcal voces.

8 Fg. 11: Error percentage for mean fundamental frequency of PRAAT (autocorrelaton) for pathologcal voce. Error percentage for mean fundamental frequency of proposed algorthm for pathologcal voce Fg. 13: Error percentage for lowest fundamental frequency of PRAAT (autocorrelaton) for pathologcal voce. Error percentage for lowest fundamental frequency of proposed algorthm for pathologcal voce Fg. 12: Error percentage for hghest fundamental frequency of PRAAT (autocorrelaton) for pathologcal voce. Error percentage for hghest fundamental frequency of proposed algorthm for pathologcal voce Fgure 12 shows that three samples exceed 40% of error by usng PRAAT_ac whle the proposed algorthm had no error that exceeds 40% of error. Fgure 13 also ndcates that proposed algorthm produces less error by havng three samples wth more than 40% of error whle the PRAAT_ac produces four samples wth more than 40% of error. As can be seen n Fg. 14, the standard devaton error for PRAAT_ac exceeds 100% for two sample pathologcal voces. 100 Fg 14: Error percentage for standard devaton of the fundamental frequency of PRAAT (autocorrelaton) for pathologcal voce. Error percentage for standard devaton of the fundamental frequency of proposed algorthm for pathologcal voce

9 But by mplementng the proposed algorthm to the same voce sample as can be seen n Fg. 15c, the ptch perod can be well determned along the sgnal thus producng a smaller error than PRAAT_ac. In both methods, the voce samples wth error of more than 20% are due to the strong subharmoncs frequences. The dsordered voce wth creaky or breathy characterstcs wll also nfluence the sgnal s waveform and snce the autocorrelaton s dependent upon the sgnal s ampltude and how correlate the perodc pattern s, the autocorrelaton functon produced wll also be dstorted. CONCLUSION The proposed method of determnng ptch provdes sgnfcant mprovement to the standard autocorrelaton whch n ths case s ndcated by the autocorrelaton by PRAAT for dsordered voce. It allows a more relable way to detect ptch, ether n low or hgh ptched voce wthout adjustng the wndow sze, fxng the ptch canddate search range and predefnng threshold lke most of the standard autocorrelaton do. (c) Fg. 15: PRAAT analyss wndow showng marks (blue lne) of ptch perod detected for the frst part of the pathologcal voce sgnal sample number 44. PRAAT analyss wndow showng marks (blue lne) of ptch perod detected for the second part of the pathologcal voce sgnal sample number 44. (c) The red lne marks ndcate each ptch perod by usng the proposed algorthm for the pathologcal voce sgnal sample number 44 Fgure 15 shows the wndow of PRAAT_ac markng the ptch perod of a pathologcal sgnal sample number 44. It can be seen n Fg. 14 that the error of the standard devaton of sample number 44 s over 1000% as well as sample number 78. As can be observed n Fg. 15a, the PRAAT_ac marked the ptch perod correctly for the frst half of the sgnal but marked the ptch perod wrongly for the second half of the sgnal shown n Fg. 15b. Ths s maybe due to the autocorrelaton used for the ptch detectng whereby the second ptch perod has hgher ACF value than the frst or the true ptch perod. Ths wll happen f the search crteron for the autocorrelaton only nvolves fndng the maxmum ACF wthn a predefned range. 101 REFERENCES Abdullah-Al-Mamun, K., F. Sarker and G. Muhammad, A hgh resoluton ptch detecton algorthm based on AMDF and ACF. J. Sc. Res., 1: DOI: /jsr.v Ahmad, S. and A.S. Spanas, Cepstrum-based ptch detecton usng a new statstcal V/UV classfcaton algorthm. IEEE Trans. Speech Audo Process., 7: DOI: / Amado, R.G. and J.V. Flho, Ptch detecton algorthms based on zero-cross rate and autocorrelaton functon for muscal notes. Proceedng of the Internatonal Conference on Audo, Language and Image Processng, Jul. 7-9, IEEE Xplore Press, Shangha, pp: DOI: /ICALIP Ca, J. and Z.Q. Lu, Robust ptch detecton of speech sgnals usng steerable flters. Proceedngs of the IEEE Internatonal Conference on Acoustcs, Speech and Sgnal Processng, Apr , IEEE Xplore Press, Munch, Germany, pp: DOI: /ICASSP Chen, S.H. and J.F. Wang, Extracton of ptch nformaton n nosy speech usng wavelet transform wth alasng compensaton. Proceedngs of the IEEE Internatonal Conference on Acoustcs, Speech and Sgnal Processng, May 7-11, IEEE Xplore Press, Salt Lake Cty, UT, USA., pp: DOI: /ICASSP

10 Chomphan, S., Effects of noses on the analyss of fundamental frequency contours for Tha speech. J. Comput. Sc., 7: DOI: /jcssp Chong, U. and Y. Shh-Chen, A ptch extracton algorthm based on LPC nverse flterng and AMDF. IEEE Trans. Acoust. Speech Sgnal Process., 25: DOI: /TASSP De Bodt, M.S., K. Ketelslagers, T. Peeters, F. L. Wuyts and F. Mertens et al., Evoluton of vocal fold nodules from chldhood to adolescence. J. Voce, 2: DOI: /j.jvoce De Chevegne, A. and H. Kawahara, YIN, a fundamental frequency estmator for speech and musc. J. Acoust. Soc. Am., 111: Donato, G.. M.S. Bartlett, J.C. Hager, P. Ekman and T.J. Sejnowsk, Classfyng facal actons. IEEE Trans. Patt. Anal. Mach. Intell., 21: DOI: / Godno-Llorente, J.I., N. Soenz-Lechon, V. Osma-Ruz, S.Agulera-Navarro and P. Gomez-Vlda, An ntegrated tool for the dagnoss of voce dsorders. Med. Eng. Phys., 28: DOI: /j.medengphy Hadjtodorov, S. and P. Mtev, A computer system for acoustc analyss of pathologcal voces and laryngeal dseases screenng. Med. Eng. Phys., 24: DOI: /S (02) Huang, H. and J. Pan, Speech Ptch Determnaton based on Hlbert-Huang transform. Sgnal Process., 86: DOI: /j.sgpro Jang, S.J., S.H. Cho, H.M. Km, H.S. Cho and Y.R. Yoon, Evaluaton of performance of several establshed ptch detecton algorthms n pathologcal voces. Proceedng of the 29th Annual Internatonal Conference of the IEEE Engneerng n Medcne and Bology Socety, Aug , IEEE Xplore Press. Lyon, pp: DOI: /IEMBS Kotnk, B., H. Hoge and Z. Kacc, Nose robust f0 determnaton and epoch-markng algorthms. Sgnal Process., 89: DOI: /j.sgpro Lahat, M., R. Nederjohn and D. Krubsack, A spectral autocorrelaton method for measurement of the fundamental frequency of nose-corrupted speech. IEEE Trans. Acoust. Speech Sgnal Process., 35: DOI: /TASSP Manfred, C., M. D'Anello, P. Bruscaglon and A. Ismaell, A comparatve analyss of fundamental frequency estmaton methods wth applcaton to pathologcal voces. Med. Eng. Phys., 22: DOI: /S (00) Mtev, P. and S. Hadjtorov, Fundamental frequency estmaton of voce of patents wth laryngeal dsorders. Inform. Sc.. 156: DOI: /S (03) Moman, P.E.N.M., Tme seres analyss model for ranfall data n Jordan: Case study for usng tme seres analyss. Am. J. Envron. Sc., 5: DOI: /ajessp Quater, T.F., Dscrete-Tme Speech Sgnal Processng. 1st Edn., Pearson Educaton Inda, Delh, ISBN: , pp: 802. Rabner, L., M. Cheng, A. Rosenberg and C. McGonegal, A comparatve performance study of several ptch detecton algorthms. IEEE Trans. Acoustcs, Speech Sgnal Process., 24: DOI: /TASSP Samad, S.A., A. Hussan and K.F. Low, Ptch detecton of speech sgnals usng the crosscorrelaton technque. IEEE Proc., 1: DOI: /TENCON Schoentgen, J., Decomposton of vocal cycle length perturbatons nto vocal jtter and vocal mcrotremor and comparson of ther sze n normophonc speakers. J. Voce, 17: DOI: /S (03) Swee, T.T., S.H.S. Salleh and M.R. Jamaludn, Speech ptch detecton usng Short-Tme Energy. Proceedngs of the Internatonal Conference on Computer and Communcaton Engneerng, May 11-12, IEEE Xplore Press, Kuala Lumpur, pp: 1-6. DOI: /ICCCE Tabrkan, J., S. Dubnov and Y. Dckalov, Maxmum a-posteror probablty ptch trackng n nosy envronments usng harmonc model. IEEE Trans. Speech Audo Process., 12: DOI: /TSA Tmmermans, B., M.S.D. Bodt, F.L. Wuyts, A. Boutewjns and G. Clement et al., Poor voce qualty n future elte vocal performers and professonal voce users. J. Voce, 16: DOI: /S (02)00108-X 102

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