Speaker Recognition Using MFCC and Hybrid Model of VQ and GMM

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 235)

Abstract

Speaker recognition is widely used for automatic authentication of speaker’s identity based on human biological features. Speaker recognition extracts, characterizes and recognizes the information about speaker identity. For feature extraction and speaker modeling many algorithms are being used. In this paper, we have proposed speaker recognition system based on hybrid approach using Mel Frequency Cepstrum Coefficient (MFCC) as feature extraction and combination of vector quantization (VQ) and Gaussian Mixture Modeling (GMM) for speaker modeling. Our approach is able to recognize speaker for both text dependent and text independent speech and uses relative index as confidence measures in case of contradiction in recognition process by GMM and VQ. Simulation results highlight the efficacy of proposed method compared to earlier work.

Keywords

Feature Extraction Feature Matching Mel Frequency Cepstral Coefficient (MFCC) Gaussian mixture modeling 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electronics enginneringSarvajanik College of Engineering and TechnologySuratIndia
  2. 2.Department of Electronics & CommunicationSarvajanik College of Engineering and TechnologySuratIndia

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