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Speech frame recognition based on less shift sensitive wavelet filter banks

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Abstract

The wavelet transform possesses multi-resolution property and high localization performance; hence, it can be optimized for speech recognition. In our previous work, we show that redundant wavelet filter bank parameters work better in speech recognition task, because they are much less shift sensitive than those of critically sampled discrete wavelet transform (DWT). In this paper, three types of wavelet representations are introduced, including features based on dual-tree complex wavelet transform (DT-CWT), perceptual dual-tree complex wavelet transform, and four-channel double-density discrete wavelet transform (FCDDDWT). Then, appropriate filter values for DT-CWT and FCDDDWT are proposed. The performances of the proposed wavelet representations are compared in a phoneme recognition task using special form of the time-delay neural networks. Performance evaluations confirm that dual-tree complex wavelet filter banks outperform conventional DWT in speech recognition systems. The proposed perceptual dual-tree complex wavelet filter bank results in up to approximately 9.82 % recognition rate increase, compared to the critically sampled two-channel wavelet filter bank.

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Authors and Affiliations

  1. Digital Multimedia Lab, Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada

    Hamid Reza Tohidypour & Amin Banitalebi-Dehkordi

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  1. Hamid Reza Tohidypour
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  2. Amin Banitalebi-Dehkordi
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Correspondence to Hamid Reza Tohidypour.

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Tohidypour, H.R., Banitalebi-Dehkordi, A. Speech frame recognition based on less shift sensitive wavelet filter banks. SIViP 10, 633–637 (2016). https://doi.org/10.1007/s11760-015-0787-z

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  • DOI: https://doi.org/10.1007/s11760-015-0787-z

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