LIE DETECTION SYSTEM USING INPUT VOICE SIGNAL K.Meena 1, K.Veena 2 (Corresponding Author: K.Veena) 1 Associate Professor, 2 Research Scholar,

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1 International Journal of Pure and Applied Mathematics Volume 117 No , ISSN: (printed version); ISSN: (on-line version) url: doi: /ijpam.v117i8.25 ijpam.eu LIE DETECTION SYSTEM USING INPUT VOICE SIGNAL K.Meena 1, K.Veena 2 (Corresponding Author: K.Veena) 1 Associate Professor, 2 Research Scholar, Department of Computer Science and Engineering, Veltech Dr. RR and Dr. SR University Chennai , India drkmeena@veltechuniv.edu.in, veenakanagaraj07@gmail.com Abstract: Investigators find lie and deception detection a significant challenge when dealing with crime cases. The process of identification of the behavior of a liar in comparison to normal human behavior has a higher percentage of relevance with respect to external behavior and the cognitive capacity of the brain. Deception detection can be achieved with accuracy by focusing on the activity of the brain as it correlates to the lying of a person. This paper discuss about lie detection system which is used to identify the lie speeches of human. This process uses preprocessing to assist in the reduction of noise and the plotting of the original artifacted EEG signals. Thus lie detection System is used to mainly focus on the following techniques, Neutral network which is used in the recognition phase and Feature Extraction Technique that is carried out by the MFCC- Mel Frequency Cepstrum Coefficients. MFCC method extract features of original EEG signals and forward features to NN technique to training and testing process. The Neural Network (NN) classifier has a much higher performance capability when compared to other similar classifiers. The primary motivation behind drafting this paper is to explore different methods that can be used for lie or deception analysis. Later, the influence of statistical features for the discrimination of thinking patterns from the normal signals is elucidated Key Words: Pre-Processing, Root Mean Square (RMS), Signal Noise Ratio (SNR), Neural Network (NN), Mel Frequency Cepstrum Coefficients (MFCC). 1. Introduction: The field of criminology is full of challenges related to investigating criminal cases, and lie detection have important applications in this field [1]. Identifying truthfulness is one of the key areas that the field of criminology focuses on since lying is a type of human character. The analysis of lying or deception properties of human beings is a critical challenge faced by crime researchers since insufficient external responses are available from learned criminals [2]. A comprehensive and efficient system with the capability to detect lies and deception can be of great assistance to researchers as well as investigators since they can easily discriminate between the different types of human responses. These responses vary and the system can successfully differentiate deceitful behavior from that which is normal. The present research work focuses on the use of input voice signal in the legal system for the detection of lies. The detection of lie during interrogations, with strategic use of available evidence, is the specific focus of this paper [3]. An automated classification of lie speech signals is described in this paper, which detects epileptic seizures, These seizures are discerned through the use of processes which are related to recognition of patterns through statistical analysis and transformation using wavelets. The test signal is calculated after preprocessing the feature vector. Countermeasures is the term which refers to a method which helps achieve the calculation of the test signal. Countermeasures refer to deliberate techniques employed by guilty people to outperform the results of the polygraph test [4]. Sometimes, however, even innocent people may use countermeasures on purpose to create an impact on the outcome of the test. The present study evaluates a specific group of features. The features were selected for the study based on their positive performance in similar studies and hence became useful for the present application [5]. A Resourceful MFCC Extraction Method in Lie Detection represents the new algorithm for feature extraction of MFCC from human speech. This novel algorithm lessens the computation power by 53% when compared with other conventional algorithm. The recreation results state of new algorithm shows that recognition accuracy of 92.93%. A reduction of 1.5% for the recognition accuracy was observed when compared with the conventional MFCC extraction algorithm shows that enhanced accuracy [6]. Neural Network utilizes a controlled training set of progress functions in a desired pattern layer [7]. ANN is denoted as artificial neutral network also known as neural network (NN) represents a mathematical or computational model that relates biological neural networks and stimulates biological neural system. The main role of systems is used to interconnect an artificial neurons for processing the information and artificial neurons using the connectionist 121

2 International Journal of Pure and Applied Mathematics approach for computation. Human voice signals are used to train the classifier. Later, feature samples belonging to two different classes were used to train the classifier, which is a Neural Network (NN), which possessed far higher performance when compared to the other classifiers. This paper focuses on a method which improves the efficiency with which a lie can be detected, in comparison to the methods reported previously. 2. Related Work: Defense-related agencies have been concerned about the uncertainty of lie-detection systems [8, 11]. This paper presents a new method which is based on the P300-based component. This method is the proposed method to be used in lie detection. Concealed information detection through the Old-ball paradigm is used as a test protocol and is designed and based on the Old-ball paradigm. Thereafter, the selection of the best combinational feature vector improves the accuracy of the classifier. Lastly, the Innocent and Guilty persons involved in the study are classified by MLP and KNN. The results obtained from this process indicate that the proposed method is capable of detecting deception with an accuracy score of 89.73%. This score is considerably superior to the methods reported previously [9, 12]. Another modality which can help understand cognitive responses including human brain thinking is referred to as electroencephalography. This method is extendable and can detect a liar from other people. Power, Root Mean Square (RMS), and Variance are statistical features which are calculated for the thinking patterns and normal patterns of the EEG signal. The primary focus area of the paper under consideration is the various methods of deception analysis and lie detection. The influence of statistical features is also explained so as to discriminate the normal signals from the thinking patterns [10, 13]. MFCC is denoted as Mel Frequency Cepstral Coefficients are extensively used in sound recognition of speech that automatically identifies the speech recognition [11, 14]. The main purpose of this work to prove the effectiveness of text independent is identifies the cepstral coefficients. Ian Lane, Weijia Shang, Jike Chong tweis and Haofeng Kou studied about MFCC and defined regulations for improve the Graphics Processing Unit proves that feature abstraction process which is appropriate for GPUs and consider the computation time are available for obtaining the performance of feature extraction on stages. It also discusses approximately in algorithm which is improved. Neural Network is organized for the request inputs which is 'direct' or relaxation process and anticipated set of outputs [12, 15]. Later, the classifier was used to classify the two classes of the extracted feature. An efficiency of 83% was achieved [13, 14]. In the process, as many as four non-mediators and eleven mediators were involved in meditation, while listening to the guidance from a master. However, ten subjects were asked to meditate themselves. The method of Bispectrum estimation was used to analyze signals from the EEG, during the process of meditation and before it. Numerous methods to set the assets of networks that are available. The method is used to set the weights obviously, using a prior knowledge. Next method is to accelerate the neural network by nourishing it instruction patterns and allowing it deviations its heaviness according to some knowledge [14, 17]. The aim of the present study is to develop an automated model that is predictive in nature and can help find out the present condition of the patient having epileptic seizures with the help of EEG signals. Segmented EEG signals can be employed to extract statistical features which can be used in the process of prediction. The strategically designed automated neural network model has the capability of classifying seizure activity into various states, including normal, interictal, and ictal. The model can achieve an accuracy of up to 99.3%. Further, a sensitivity score at a maximum 100% can be achieved and a specificity score of 98.3% for all the classes is also available. The model of Artificial Neural networks unraveled that a far surpassing model of validation for the classification is available, which can work with an optimum number of neurons and a different set of parameters [15, 17]. The Guilty Knowledge Test has been used by the lie detector based on the forensic electroencephalogram (EEG) as a method which is a potential and forceful substitute to the classical question test which is comparative in nature. The evaluation of this method requires the participation of several participants who have gone through the guilty knowledge test paradigm. Their brain signals were subsequently recorded. This paper proposes a method which improves the efficiency of the lie detection method in comparison to the previously reported methods [16, 18]. An analysis of the current cases and science, which is balanced in nature, where litigants have attempted to introduce human input voice-based deception detection is provided. Key limitations of the science are identified which serve as expert evidence, and the problems arising from scientific evidence prior to proof for its validity and reliability are explored. 3. Proposed Method 3.1 Overview: Acquiring the input voice signals recording of the tested person through the use of a Concealed Information Test is the core idea of the method. After performing signal acquisition, some of the basic filtering operations on the acquired data help increase the SNR of the signal and also reduce the percentage of noise. Sample input Classification Guilty Knowledge test (GKT) Training and Testing Preprocess Feature Extraction Forwards Features to NN Classifier Figure 1: Overall System of Lie Detection System 122

3 Accuracy International Journal of Pure and Applied Mathematics 3.2 MFCC- MEL Frequency Cepstrum Coefficient MFCC is most familiar technique that is widely used in feature extraction of sound signal. The audio signal is mainly found on MFCC method. The audio signal is tested with the codebook and exacting species sound which will be recognized. This is used for sound recognition systems. The input file goes inside a MFCC feature extraction. Training backgrounds for each input voice of human noise were advanced for availability of MFCC matrices. Usually sound recognition system is the basic level of feature extraction and feature matching. The main objective was to apply Mel Frequency Cepstral coefficient for identification of lie speech from human input voices. The process is working out of MFCC structures for assumed input signal contains the following steps: 1. Input signals were distributed into overlapping frames. 2. To prevent break in each frame hamming windows are used. 3. Power spectrum is used to calculate the signal frames. 4. The filter bank for each window is calculated from power spectrum samples. 5. The logarithm computation of found coefficients and computation of separate cosine transform. 3.4 Neural Networks And Lie Detection The network generation are done overcome a classification problems using neural networks (NN). The training of red palm weevil sounds and testing process are done with Neural Network (NN) method. Three layers are present in Neural Network. The extracted features is grants a first layer which computes the spaces of input vector and training input vectors. This will helps as the production of vector elements showing the training input. The second layer summarizes each class of inputs to produce a vector possibility as its overall output. Each human input voice signals are competent with system. The process is done to improve the lie detection from human voice signal. Training of system was carried out with noise free environment to get the results are very appropriately. Based on training system, the voice signal of human speeches is used for sound recognition process. Two kinds of sounds are used for recognition. Recognized sounds: The input sound is used for training. Unrecognized sounds: The sounds are not used for training. The algorithm is used in this method as Back Propagation Network. This algorithm contains some simple steps which are used to categorization and identical input sound signals with creative signal. 1. Loading weights (Set to arbitrary variables with zero mean and variance one). 2. While suspending condition is made-up do Step For both training pair organize Steps. 4. Each input unit (X i,i=1,..,n) collects input signal xi and shows this signal to all units in the layer above. 5. Each blocked unit (Z j j=1,,p) calculations its weighted input signals showing the activation to compute its output signal and allows these to all units in the layer above (output units). 4. Result And Discussion: Test input in epileptic seizure detection is the voice signal in the testing phase. Figure 5 of Table 1 shows the results accuracy after experimentation of the proposed approach. Table 1: Performance for Feature Extraction Techniques Feature Extraction Techniques Gaussian mixture modeling (GMM) Hidden Markov Model (HMM) Mel Frequency Cepstrum Coefficients (MFCC) Accur acy (%) Total time taken to build model (in seconds) 87% % % 0.5 The Table 1 shows performance of human input voice over feature extraction and showing comparison with existing techniques Hidden Markov Model (HMM), Mel Frequency Cepstrum Coefficients (MFCC) and Gaussian mixture modeling (GMM). The proposed system using MFCC is producing better output than any other available techniques MFCC HMM GMM Feature Extraction Techniques Figure 2: Performance for Feature Extraction Techniques Figure3 shows the original waveform of input speech signal. In order to evaluate the speech, the silence sound must be removed and it is done by using the preprocess method in figure4. Figure 5 shows the feature extraction method and Figure 6 shows that neural network is used to train and test the speech signal. 123

4 International Journal of Pure and Applied Mathematics The figure 7 shows that lie recognition results and this classification process shows the result based on input speech signal (Normal or Lie). Figure 3: Human input signal Figure 4: Preprocess of Human input signal Figure 5: Feature Extraction of Human input signal 5. Conclusion Brain signals can be understood with respect to the stimulus through the use of an efficient method, which is also known as electroencephalography. Lie detection, which is a type of internal stimulus, induces a response where the respective portion of the brain is activated. Deception status can be analyzed with the help of the thinking stimulus and is related to the lie portion of the brain. The EEG dataset was used to perform preprocessing prior to the process of detection. This preprocessing was followed by the training of the Neural Network (NN) and the test related to it. Thereafter MFCC is applied and the response features are extracted. Statistical features including Variance, Power, and RMS are calculated for the thinking and normal portions of the Voice input signal. There is a higher influence of temporal lobe signals because of the thinking behavior. Therefore, the NN classifier helps view the differences between responses which could be either deceptive or truthful. Acknowledgement: Lie and deception detection is a great challenge in today`s world when dealing with criminal cases. Lie detection helps when dealing with cyber crime detection. Hence I like to help the detection of the criminals using the voice signal as the input. I am thankful to the people who have helped me in writing this paper. References [1] Daniel D. Langleben, Jane Campbell Moriarty Using Brain Imaging for Lie Detection: Where Science, Law, and Policy Collide Psychology, Public Policy, and Law American Psychological Association [2] M. RajyaLakshmi, T. Kameswara Rao, Dr. T. V. Prasad, Exploration of Recent Advances in The Field of Brain Computer Interface, IOSR J. of Computer Engg., Vol.8, Issue 2, [3] Deng Wang, Duoqian Miao, and Gunnar Blohm. A New Method for EEG-Based Concealed Information Test. IEEE transactions on information forensics and security, March Figure 6: Trained and Testing of Neural Network [4] Aruna Tyagi and Vijay Nehra, Brain computer interface: a thought translation device turning fantasy into reality, Int. J. Biomedical Engg. and Tech., Vol. 11, No. 2, 2013 [5] P. K. Padhy,Avinash Kumar, Vivek Chandra, Kalyan Rao Thumula and A. Kumar, Feature Extraction and Classification of Brain Signal, World Acad. of Sc., Engg. and Tech Figure 7: Classification Result [6] Tomi Kinnunen. and Rahim Saeidi Low- Variance Multitaper MFCC Features: A Case Study in Robust Speaker Verification. IEEE Transactions on Speech, Audio and Language Processing. 124

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