International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 02 Issue: 02 | May-2015 www.irjet.net p-ISSN: 2395-0072 © 2015, IRJET.NET- All Rights Reserved Page 444 Development and Implementation of Algorithm for Speaker recognition for Gujarati Language Jigarkumar Patel 1 , Arun Nandurbarkar 2 1 PG student, Electronics and Communication, L.D college of engg, Gujarat, India 2 Associate Professor, Electronics and Communication, L.D college of engg, Gujarat, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Modern speech understanding systems merge interdisciplinary technologies from Signal Processing, Pattern Recognition, Natural Language and Linguistics into a unified statistical framework. In this paper weighted MFCC(Mel frequency cepstral coefficients) and GMM(Gaussian Mixture Model) are implemented for Speaker Recognition in Gujarati Language. The experimental database consists of 30 speakers, 10 female and 20 male, collected in sound proof room. The result of this experiment certificates that this technique works better for speaker recognition for Gujarati language than only traditional MFCC with GMM. Key Words: speaker recognition; Mel frequency cepstral coefficients; feature extraction; weighted Mel frequency cepstral coefficients; Gaussian Mixture Model; maximum likelihood. 1. INTRODUCTION Modern speech understanding systems merge interdisciplinary technologies from Signal Processing, Pattern Recognition, Natural Language and Linguistics into a unified statistical framework. These systems, which have applications in a wide range of signal processing problems, represent a revolution in Digital Signal Processing(DSP)[1][2]. Once a field dominated by vector- oriented processors and linear algebra bases mathematics, the current generation of DSP-based systems rely on sophisticated statistical models implemented using a complex software paradigm. Such systems now capable of understanding continuous speech input for vocabularies of several thousand words in operational environments. Speech signal processing technology is an indispensable technology in the information society, and speaker recognition is an important research field of speech processing. Speaker recognition is also called the voiceprint recognition, which makes it possible to identify or verify the identity of the speaker using the speech feature. It combines the theories of various subjects, such as acoustics, phonetics, linguistics, physiology, digital signal processing, pattern recognition and artificial intelligence etc. Speaker recognition has a wide application prospect in the judicial identification, security Monitoring, e-commerce and other fields. The extraction of the Mel frequency cepstral coefficients is one of the popular approaches of feature extraction. Speaker modeling is the main part of a speaker recognition system. The Gaussian mixture model (GMM) is the most common approach for speaker modeling in text- independent speaker recognition[4][5]. A general speaker recognition system, shown in Figure 1, consists mainly, of three stages, each stages are explained in next sections. Models speech Xt Speaker wave ID Figure 1. Speaker Recognition System[5]. 2. The Feature Extraction 2.1 MFCC(Mel Frequency Cepstral Coefficients) The purpose of feature extraction is to convert the speech waveform to a set of features for further analysis. Where appropriate information is estimated in a suitable form and size, from the speech signal to obtain a good representation of the speaker features, (Mel Frequency Cepstral Coefficients (MFCC features) are chosen in this paper because they are based on the perceptual characteristics of the human auditory system[4], figure 2 shows a block diagram of the steps in Mel feature extraction. Figure 2. MFCC feature extraction block diagram Feature Extraction Classification Training mel cepstrum mel spectrum frame continuous speech Frame Blocking Windowing FFT spectrum Mel-frequency Wrapping Cepstrum