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)


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.


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.


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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

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