International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 04 | Apr-2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET ISO 9001:2008 Certified Journal Page 709 A Survey on Speaker Recognition With Various Feature Extraction And Classification Techniques Jyoti B. Ramgire 1 , Prof. Sumati M.Jagdale 2 1 PG Student, Dept. Of Electronics and Telecommunication Engineering, Bharati Vidyapeeth’s College of Engineering for Women, Pune 43, Maharashtra, India 2 Associate Professor, Dept. Of Electronics and Telecommunication Engineering, Bharati Vidyapeeth’s College of Engineering for Women, Pune 43, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Speech processing is more popular day by day for providing immense security. Authentication purpose speech is widely used. Speaker recognition is the process which can verify and identify the person who is speaking. Speech recognition system is different than speaker recognition system. Speaker recognition are widely used in industries, hospital, laboratory etc. Its advantages are more secure, easy implementation, more user friendly. For the area where security is very important, speaker recognition technique is one of the most widely used technique. It is also popular biometric technique. This paper describes an overview of different techniques that can be used in application of speaker recognition such as LPC, LPCC MFCC etc. Also discuss on different classifiers such as DTW, GMM, VQ, SVM. The main objective of this review paper is to summarize well known techniques for speaker recognition system. Key Words: Speaker recognition, Mel frequency cepstral coefficients(MFCC), Linear predictive coding (LPC), Linear Predictive Cepstral Coefficients (LPCC), Gaussian Mixture Model(GMM), Vector Quantization(VQ), Support Vector Machine(SVM), Dynamic Time Warping(DTW) 1. INTRODUCTION Speech signal contains different levels of information[14]. Speech signal can be used for speech recognition, speaker recognition or voice command recognition system[3]. Speaker recognition is used for many speech processing applications especially security and authentication. Today security is major requirement. Sometimes there may be confusion regarding speech and speaker recognition. Speaker recognition and speech recognition are very closely related systems but these two systems are different[14]. Speech recognition is the process of recognizing what is being said and speaker recognition is the process of recognizing who is speaking. Speech recognition has ability to automatically recognizing the spoken words of person based on information in speech signal[3].. Speaker recognition is classified as speaker identification and verification. The main aim of speaker recognition is to identify the speaker by extraction, characterization and recognition of the information contained in speech signal[14]. Speech recognition consist of speaker dependent and speaker independent. The human speech is processed by machine depending on feature extraction and feature matching. Basic model of speaker recognition is shown in Figure 1[3]. Fig -1: Basic model of Speaker Recognition system Speaker recognition process is done in three steps. First is pre-processing is used to remove silent period from speech signal[3]. In speaker recognition, the feature is extracted using different techniques such as Linear predictive coding(LPC), Linear Predictive Cepstral Coefficients (LPCC), Mel frequency cepstral coefficients MFCC. For feature classification different classifiers are used such as Support Vector Machine (SVM), Vector Quantization(VQ), Gaussian Mixture Model(GMM), Dynamic Time Warping(DWT). 2. RELATED WORK Table -1: Literature Survey Author Name Feature Extraction Classifiers Advantages V. Tiwari et.al.[1] LPC.LDB, MFCC VQ 1. MFCC with hanning window using 32 filter has more efficiency. 2. Density matching property of vector quantization is powerful K. Kaur, et.al.[2] LPC, LPCC MFCC VQ, GMM, SVM,DWT ,HMM 1.MFCC technique is more consistent with human hearing as compared to LPCC, MFCC. 2. GMM is best