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Computer Recognition Systems 2 pp 445–453Cite as

Automatic Detection of Disorders in a Continuous Speech with the Hidden Markov Models Approach

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Part of the book series: Advances in Soft Computing ((AINSC,volume 45))

Abstract

Hidden Markov Models are widely used for recognition of any patterns appearing in an input signal. In the work HMM’s were used to recognize two kind of speech disorders in an acoustic signal: prolongation of fricative phonemes and blockades with repetition of stop phonemes.

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References

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Maria Curie-Sklodowska University, Pl Marii Curie-Sklodowskiej 1, 20-031, Lublin, Poland

    Marek Wiśniewski, Wieslawa Kuniszyk-Jóźkowiak, Elzbieta Smołka & Waldemar Suszyński

Authors
  1. Marek Wiśniewski
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  2. Wieslawa Kuniszyk-Jóźkowiak
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  3. Elzbieta Smołka
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  4. Waldemar Suszyński
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Editor information

Editors and Affiliations

  1. Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Marek Kurzynski , Edward Puchala , Michal Wozniak  & Andrzej Zolnierek , ,  & 

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© 2007 Springer-Verlag Berlin Heidelberg

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Wiśniewski, M., Kuniszyk-Jóźkowiak, W., Smołka, E., Suszyński, W. (2007). Automatic Detection of Disorders in a Continuous Speech with the Hidden Markov Models Approach. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds) Computer Recognition Systems 2. Advances in Soft Computing, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75175-5_56

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  • DOI: https://doi.org/10.1007/978-3-540-75175-5_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75174-8

  • Online ISBN: 978-3-540-75175-5

  • eBook Packages: EngineeringEngineering (R0)

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