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Fundamental Frequency Extraction in Speech Emotion Recognition

  • Conference paper
Multimedia Communications, Services and Security (MCSS 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 287))

Abstract

Emotion recognition in a speech signal has received much attention recently, due to its usefulness in many applications associated with human – computer interaction. Fundamental frequency recognition in a speech signal is one of the most crucial factors in successful emotion recognition. In this work, parameters of an autocorrelation – based algorithm for fundamental frequency detection are analysed on the example of Berlin emotion speech database (EMO-DB). The obtained results show that lower-than-standard values of the upper limit of the analysed frequency range tend to improve the classification outcome. Statistics of prosody contours and Mel-frequency cepstral coefficients (MFCC) have been used for feature set construction and support vector machine (SVM) has been used as a classifier, yielding high recognition rates.

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Author information

Authors and Affiliations

  1. Institute of Information Technology, Technical University of Łódź, ul. Wólczańska 215, 90-924, Łódź, Poland

    Bartłomiej Stasiak & Krzysztof Rychlicki-Kicior

Authors
  1. Bartłomiej Stasiak
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  2. Krzysztof Rychlicki-Kicior
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Editor information

Editors and Affiliations

  1. Department of Telecommunications, AGH University of Science and Technology, Krakow, Poland

    Andrzej Dziech

  2. Multimedia Systems Department, Gdansk University of Technology, Narutowicza 11/22, 80-233, Gdansk, Poland

    Andrzej Czyżewski

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© 2012 Springer-Verlag Berlin Heidelberg

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Stasiak, B., Rychlicki-Kicior, K. (2012). Fundamental Frequency Extraction in Speech Emotion Recognition. In: Dziech, A., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2012. Communications in Computer and Information Science, vol 287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30721-8_29

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  • DOI: https://doi.org/10.1007/978-3-642-30721-8_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30720-1

  • Online ISBN: 978-3-642-30721-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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