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Adaptive bands filter bank optimized by genetic algorithm for robust speech recognition system
Taiyuan University of Technology.
Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology, Media and Information Technology. (Media and Information Technology)
Taiyuan University of Technology.
2011 (English)In: Journal of Central South University of Technology, ISSN 1005-9784, E-ISSN 1993-0666, Vol. 18, no 5, 1595-1601 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Verlag (Germany) , 2011. Vol. 18, no 5, 1595-1601 p.
Keyword [en]
perceptual filter banks, bark scale, speaker independent speech recognition systems, zero-crossing peak amplitude, genetic algorithm
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-71640DOI: 10.1007/s11771-011-0877-1ISI: 000295611900038OAI: diva2:451827

Funding Agencies|National Natural Science Foundation of China|61072087|Shanxi Provincial Graduate Innovation Fund of China|20093048|

Available from: 2011-10-27 Created: 2011-10-27 Last updated: 2013-04-25

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Evangelista, Gianpaolo
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