Adaptive bands filter bank optimized by genetic algorithm for robust speech recognition system

  • Li-xia Huang (黄丽霞)
  • G. Evangelista
  • Xue-ying Zhang (张雪英)
Article
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Abstract

Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems. However, the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open. Owing to spectral analysis in feature extraction, an adaptive bands filter bank (ABFB) is presented. The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm (GA) to optimize the design parameters. The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop. The deployment of ABFB together with zero-crossing peak amplitude (ZCPA) feature as a front process for radial basis function (RBF) system shows significant improvement in robustness compared with the Bark-scale filter bank. In ABFB, several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria. For the ease of optimization, only symmetrical bands are considered here, which still provide satisfactory results.

Key words

perceptual filter banks bark scale speaker independent speech recognition systems zero-crossing peak amplitude genetic algorithm 
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Copyright information

© Central South University Press and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Li-xia Huang (黄丽霞)
    • 1
    • 2
  • G. Evangelista
    • 2
  • Xue-ying Zhang (张雪英)
    • 1
  1. 1.Department of Information and EngineeringTaiyuan University of TechnologyTaiyuanChina
  2. 2.Department of Science Technology (ITN)Linköping UniversityNorrköpingSweden

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