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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Feature Extraction Based on Speech Attractors in the Reconstructed Phase Space for Automatic Speech Recognition Systems

  • Shekofteh, Yasser (Biomedical Engineering Department, Amirkabir University of Technology) ;
  • Almasganj, Farshad (Biomedical Engineering Department, Amirkabir University of Technology)
  • Received : 2012.01.31
  • Accepted : 2012.07.09
  • Published : 2013.02.01
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Abstract

In this paper, a feature extraction (FE) method is proposed that is comparable to the traditional FE methods used in automatic speech recognition systems. Unlike the conventional spectral-based FE methods, the proposed method evaluates the similarities between an embedded speech signal and a set of predefined speech attractor models in the reconstructed phase space (RPS) domain. In the first step, a set of Gaussian mixture models is trained to represent the speech attractors in the RPS. Next, for a new input speech frame, a posterior-probability-based feature vector is evaluated, which represents the similarity between the embedded frame and the learned speech attractors. We conduct experiments for a speech recognition task utilizing a toolkit based on hidden Markov models, over FARSDAT, a well-known Persian speech corpus. Through the proposed FE method, we gain 3.11% absolute phoneme error rate improvement in comparison to the baseline system, which exploits the mel-frequency cepstral coefficient FE method.

Keywords

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