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VQ/GMM-Based Speaker Identification with Emphasis on Language Dependency

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Advanced Computing and Systems for Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 883))

Abstract

The biometric recognition of human through the speech signal is known as automatic speaker recognition (ASR) or voice biometric recognition. Plenty of acoustic features have been used in ASR so far, but among them Mel-frequency cepstral coefficients (MFCCs) and Gammatone frequency cepstral coefficients (GFCCs) are popularly used. To make ASR language and channel independent (if training and testing microphones and languages are not same), i-Vector feature and unwanted variability compensation techniques like linear discriminative analysis (LDA) or probabilistic LDA (PLDA), within-class covariance normalization (WCCN) are extensively used. At the very present days, the techniques for modeling/classification that are used are Gaussian mixture models (GMMs), vector quantization (VQ), hidden Markov model (HMM), deep neural network (DNN), and artificial neural network (ANN). Sometimes, model-domain normalization techniques are used to compensate unwanted variability due to language and channel mismatch in training and testing data. In the present paper, we have used maximum log-likelihood (MLL) to evaluate the performance of ASR on the databases(DBs), namely ELSDSR, Hyke-2011, and IITG-MV SR Phase-I & II, based on MFCCs and VQ/GMM where the scoring technique MLL is used for the recognition of speakers. The experiment is carried out to examine the language dependency and environmental mismatch between training and testing data.

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Acknowledgements

This project is partially supported by the CMATER laboratory of the Computer Science and Engineering Department, Jadavpur University, India, TEQIP-II, PURSE-II and UPE-II projects of Govt. of India.

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Authors and Affiliations

  1. Jadavpur University, Kolkata, 700032, India

    Bidhan Barai, Debayan Das, Nibaran Das, Subhadip Basu & Mita Nasipuri

Authors
  1. Bidhan Barai
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  2. Debayan Das
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  3. Nibaran Das
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  4. Subhadip Basu
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  5. Mita Nasipuri
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Corresponding author

Correspondence to Bidhan Barai .

Editor information

Editors and Affiliations

  1. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Rituparna Chaki

  2. Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari, Mestre, Venice, Venezia, Italy

    Agostino Cortesi

  3. Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  4. Department of Computer Science and Engineering, University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

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Barai, B., Das, D., Das, N., Basu, S., Nasipuri, M. (2019). VQ/GMM-Based Speaker Identification with Emphasis on Language Dependency. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 883. Springer, Singapore. https://doi.org/10.1007/978-981-13-3702-4_8

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