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On an Advanced System of Speech Recognition Based on Gammachirp Wavelet Transform as Filter Bank

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Several techniques of parameterization in speech recognition have been developed to modeled human auditory system such as the Mel Frequency Cepstral MFCC and Perceptual Linear Predictive PLP which dominate the speech recognition fields. The success of acoustic features derived from MFCC and PLP turned them into a standard choice in specific conditions but their performances degrade with additive noise. Recently, we have proposed an auditory feature extractor based on gammachirp wavelet transform, they are obtained by replacement of the filter bank used in above methods by a gammachirp wavelet transforms. We found that proposed feature give a significant improvement in robust speech recognition than conventional acoustic feature.
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Gammachirp; Wavelet Transform; Hidden Markov Models; Perceptual Linear Predictive

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