Advertisement

Speaker Recognition Using Gaussian Mixtures Models

  • Eric Simancas-Acevedo
  • Akira Kurematsu
  • Mariko Nakano Miyatake
  • Hector Perez-Meana2
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)

Abstract

Control access to secret or personal information by using the speaker voice transmitted by long distance communication systems, such as the telephone system, requires accuracy and robustness of the identification or identity verification system, since the speech signal is distorted during the transmission process. Taking in consideration these requirements, a robust text independent speaker identifications system is proposed in which the speaker features are extracted using the Lineal Prediction Cepstral Coefficients (LPCEPSTRAL) and the Gaussian Mixture Models, which provides the features distribution and estimates the optimum model for each speaker, is used for identification. The proposed system, was evaluate using a data-base of 80 different speakers, with a pronoun phrase of 3-5s and digits in Japanese language stored during 4 months. Evaluation results show that proposed system achieves more than 90% of recognition rate.

Keywords

Hide Markov Model Recognition Rate Speech Signal Gaussian Mixture Model Dynamic Time Warping 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Douglas A. Reynolds, “Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models”, IEEE Transactions on Speech and Audio Processing, Vol. 3,No.1, January 1995.Google Scholar
  2. [2]
    Richard J. Mammone, Xiaoyu Zhang, Ravi P. Ramachandran, “Robust Speaker Recognition”, IEEE Signal Processing Magazine, September 1996.Google Scholar
  3. [3]
    Douglas O’ Shaughnessy, “Sppech Communication”Google Scholar
  4. [4]
    Lawrence Rabiner, Biing-Hwang Juang, “Fundamentals Of Speech Recognition”, Prentice Hall, New Jersey, 1993.Google Scholar
  5. [5]
    Joseph Picone, “Signal Modeling Techniques In Speech Recognition”, Procceding of the IEEE, Jun 3, 1993.Google Scholar
  6. [6]
    Steve Young, D. Kershaw, J. Odell, D. Ollason, V. Valtchev, P. Woodland, “The HTK Book (for HTK Version 3.0) ”, Microsoft Corporation, July, 2000.Google Scholar
  7. [6]
    E. Simancas, M. Nakano Miyatake and H. Perez.Meana, “Speaker Verification Using Pitch and Melspec Information”, To appear in The Journal of Telecommunications and Radio Engineering, 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Eric Simancas-Acevedo
    • 2
  • Akira Kurematsu
    • 1
  • Mariko Nakano Miyatake
    • 2
  • Hector Perez-Meana2
    • 2
  1. 1.The University of Electro-CommunicationsTokyoJapan
  2. 2.National Polytechnic Institute of MexicoMexico CityMexico

Personalised recommendations