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Some Like It Gaussian. . .

  • Conference paper
  • First Online:
Text, Speech and Dialogue (TSD 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2448))

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Abstract

In Hidden Markov models, speech features are modeled by Gaussian distributions. In this paper, we propose to gaussianize the features to better fit to this modeling. A distribution of the data is estimated and a transform function is derived. We have tested two methods of the transform estimation (global and speaker based). The results are reported on recognition of isolated Czech words (SpeechDat-E) with CI and CD models and on medium vocabulary continuous speech recognition task (SPINE). Gaussianized data provided in all three cases results superior to standard MFC coefficients proving, that the gaussianization is a cheap way to increase the recognition accuracy

Supported by Grant Agency of Czech Republic under project No. 102/02/0124.

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References

  1. B. Gold and N. Morgan. Speech and audio signal processing. John Wiley & Sons, 2000.

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Author information

Authors and Affiliations

  1. Faculty of Elec. Eng. and Communication, VUT Brno, Brno

    Pavel Matějka

  2. Fac. of Inf. Technology, VUT Brno, Brno

    Petr Schwarz, Martin Karafiát & Jan Černocký

Authors
  1. Pavel Matějka
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  2. Petr Schwarz
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  3. Martin Karafiát
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  4. Jan Černocký
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Editor information

Editors and Affiliations

  1. Faculty of Informatics Department of Programming Systems and Communication, Masaryk University, Botanická 68a, 602 00, Brno, Czech Republic

    Petr Sojka

  2. Faculty of Informatics Department of Information Technologies, Masaryk University, Botanická 68a, 602 00, Brno, Czech Republic

    Ivan Kopeček  & Karel Pala  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Matějka, P., Schwarz, P., Karafiát, M., Černocký, J. (2002). Some Like It Gaussian. . .. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2002. Lecture Notes in Computer Science(), vol 2448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46154-X_44

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  • DOI: https://doi.org/10.1007/3-540-46154-X_44

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44129-8

  • Online ISBN: 978-3-540-46154-8

  • eBook Packages: Springer Book Archive

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