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International Workshop on Text, Speech and Dialogue

TSD 1999: Text, Speech and Dialogue pp 229–234Cite as

Developing HMM-Based Recognizers with ESMERALDA

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1692))

Abstract

ESMERALDA is an integrated environment for the development of speech recognition systems. It provides a powerful selection of methods for building statistical models together with an efficient incremental recognizer. In this paper the approaches adopted for estimating mixture densities, Hidden Markov Models, and n-gram language models are described as well as the algorithms applied during recognition. Evaluation results on a speaker independent spontaneous speech recognition task demonstrate the capabilities of ESMERALDA.

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Author information

Authors and Affiliations

  1. Faculty of Technology, University of Bielefeld, P.O. Box 100131, 33501, Bielefeld, Germany

    Gernot A. Finkco]

Authors
  1. Gernot A. Finkco]
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineerig, Faculty of Applied Sciences, University of West Bohemia in Plzeň, Universitní 22, 306 14, Pizeň, Czech Republic

    Václav Matousek , Pavel Mautner  & Jana Ocelíková ,  & 

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

    Petr Sojka

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© 1999 Springer-Verlag Berlin Heidelberg

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Finkco], G.A. (1999). Developing HMM-Based Recognizers with ESMERALDA. In: Matousek, V., Mautner, P., Ocelíková, J., Sojka, P. (eds) Text, Speech and Dialogue. TSD 1999. Lecture Notes in Computer Science(), vol 1692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48239-3_42

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  • DOI: https://doi.org/10.1007/3-540-48239-3_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66494-9

  • Online ISBN: 978-3-540-48239-0

  • eBook Packages: Springer Book Archive

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