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An evolutionary decoding method for HMM-based continuous speech recognition systems using particle swarm optimization

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Abstract

The main recognition procedure in modern HMM-based continuous speech recognition systems is Viterbi algorithm. Viterbi algorithm finds out the best acoustic sequence according to input speech in the search space using dynamic programming. In this paper, dynamic programming is replaced by a search method which is based on particle swarm optimization. The major idea is focused on generating initial population of particles as the speech segmentation vectors. The particles try to achieve the best segmentation by an updating method during iterations. In this paper, a new method of particles representation and recognition process is introduced which is consistent with the nature of continuous speech recognition. The idea was tested on bi-phone recognition and continuous speech recognition workbenches and the results show that the proposed search method reaches the performance of the Viterbi segmentation algorithm ; however, there is a slight degradation in the accuracy rate.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

    Negin Najkar & Farbod Razzazi

  2. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Hossein Sameti

Authors
  1. Negin Najkar
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  2. Farbod Razzazi
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  3. Hossein Sameti
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Correspondence to Negin Najkar.

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Najkar, N., Razzazi, F. & Sameti, H. An evolutionary decoding method for HMM-based continuous speech recognition systems using particle swarm optimization. Pattern Anal Applic 17, 327–339 (2014). https://doi.org/10.1007/s10044-012-0313-7

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  • DOI: https://doi.org/10.1007/s10044-012-0313-7

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