Vector Quantization in Language Independent Speaker Identification Using Mel-Frequency Cepstrum Co-efficient

  • D. AmbikaEmail author
  • V. Radha
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 284)


Speaker recognition is a process of recognizing a person based on their unique voice signals and it is a topic of great importance in areas of intelligent and security. Considerable research and development has been carried out to extract speaker specific features and to develop features matching techniques. The goal of this paper is to perform text-independent speaker identification. These models rely on Mel Frequency Cepstral Coefficients (MFCC) for extraction of speaker specific features and for speaker modelling Vector Quantization (VQ) is used due to high accuracy and simplicity. The proposed system efficiency was analyzed by using 20 filter banks for extracting features. The performance was evaluated using MATLAB against different speakers in different languages such as Tamil, Malayalam, Hindi, Telugu and English with duration of 2, 3 and 4 s. Experimental result shows that 4 s duration of speech regardless of language is able to produce 98 %, 99 % and 97 % of identification when compared to 2 and 3 s. The system efficiency may further be improved using other speaker modelling techniques like Neural Network, Hidden Markov Model and Gaussian Mixture Model.


  1. 1.
    S. Furui, Speaker-independent and speaker-adaptive recognition techniques, in Advances in Speech Signal Processing, ed. by S. Furui, M.M. Sondhi (Marcel Dekker, New York, 1991), pp. 597–622Google Scholar
  2. 2.
    S. Furui, Recent advances in speaker recognition, in Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication, 1997, pp. 237–252Google Scholar
  3. 3.
    S. Furui, Digital Speech Processing, Synthesis, and Recognition, 2nd edn. (Marcel Dekker, New York, 2000)Google Scholar
  4. 4.
    D.A. Reynolds, Automatic Speaker Recognition: Current Approaches and Future Trends (MIT Lincoln Laboratory, Lexington, 2006)Google Scholar
  5. 5.
    M. Sigmund, Voice Recognition by Computer (Tectum Verlag DE, Marburg, 2003)Google Scholar
  6. 6.
    H.S. Jayanna, S.R.M. Prasanna, Analysis, feature extraction, modeling and testing techniques for speaker recognition. IETE Tech. Rev. 26, 181–190 (2009)CrossRefGoogle Scholar
  7. 7.
    A.N. Sigappi, S. Palanivel, Spoken word recognition strategy for Tamil language. IJCSI Int. J. Comput. Sci. Issues, 9(1), No. 3 (2012). ISSN 1694-0814Google Scholar
  8. 8.
    M.G. Sumithra, K. Thanuskodi, A new speaker recognition system with combined feature extraction techniques. J. Comput. Sci., 7(4), 459–465 (2011), Science Publications. ISSN 1549-3636Google Scholar
  9. 9.
    Y. Goto, T. Akatsu et al., An investigation on speaker vector-based speaker identification under noisy conditions, in Proceedings of the International Conference on Audio, Language and Image Processing, IEEE Xplore, pp. 1430–1435Google Scholar
  10. 10.
    S. Menon, M. Lech, N. Maddage, Speaker verification based on different vector quantization techniques with Gaussian mixture models, in Proceedings of the 3rd International Conference on Network and System Security, pp. 403–408Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceAvinashilingam Institute for Home Science and Higher Education for WomenCoimbatoreIndia

Personalised recommendations