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Evolutionary Splines for Cepstral Filterbank Optimization in Phoneme Classification

Abstract

Mel-frequency cepstral coefficients have long been the most widely used type of speech representation. They were introduced to incorporate biologically inspired characteristics into artificial speech recognizers. Recently, the introduction of new alternatives to the classic mel-scaled filterbank has led to improvements in the performance of phoneme recognition in adverse conditions. In this work we propose a new bioinspired approach for the optimization of the filterbanks, in order to find a robust speech representation. Our approach—which relies on evolutionary algorithms—reduces the number of parameters to optimize by using spline functions to shape the filterbanks. The success rates of a phoneme classifier based on hidden Markov models are used as the fitness measure, evaluated over the well-known TIMIT database. The results show that the proposed method is able to find optimized filterbanks for phoneme recognition, which significantly increases the robustness in adverse conditions.

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Correspondence to Leandro D. Vignolo.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Vignolo, L.D., Rufiner, H.L., Milone, D.H. et al. Evolutionary Splines for Cepstral Filterbank Optimization in Phoneme Classification. EURASIP J. Adv. Signal Process. 2011, 284791 (2011). https://doi.org/10.1155/2011/284791

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Keywords

  • Success Rate
  • Information Technology
  • Markov Model
  • Evolutionary Algorithm
  • Hide Markov Model