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Evolution, learning and speech recognition in changing acoustic environments

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Parallel Problem Solving from Nature — PPSN V (PPSN 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1498))

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Abstract

In this paper, we apply Evolutionary Algorithms (EA) to evolve Automatic Speech Recognition Systems (ASRSs) in order to adapt them to acoustic environment changes. The general framework relates to the Evolutionary paradigm and it addresses the problem of robustness of speech recognition as a two level process. First, some initial ASRSs based on feedforward Artificial Neural Networks (ANNs) are designed and trained with an initial speech corpus. Second, the ASRSs are tested in Virtual Acoustic Environments (VAEs) in which we playback some speech test data. By using Evolutionary Operators as mutation, crossover and selection, the adaptation of initial ASRSs to a new VAE is achieved. The VAE includes different real world noises and are physical models of real rooms (1 floor, 1 ceiling and 4 walls) thanks to image methods of sound propagation in small rooms.

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Authors and Affiliations

  1. CLIPS-IMAG Laboratory, Joseph Fourier University, BP 53, 38041, Grenoble cedex 9, France

    Anne Spalanzani & Harouna Kabré

Authors
  1. Anne Spalanzani
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  2. Harouna Kabré
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Editor information

Agoston E. Eiben Thomas Bäck Marc Schoenauer Hans-Paul Schwefel

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© 1998 Springer-Verlag Berlin Heidelberg

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Spalanzani, A., Kabré, H. (1998). Evolution, learning and speech recognition in changing acoustic environments. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056908

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  • DOI: https://doi.org/10.1007/BFb0056908

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65078-2

  • Online ISBN: 978-3-540-49672-4

  • eBook Packages: Springer Book Archive

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