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Fundamental Frequency Extraction in Speech Emotion Recognition

  • Bartłomiej Stasiak
  • Krzysztof Rychlicki-Kicior
Part of the Communications in Computer and Information Science book series (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.

Keywords

speech emotion recognition pitch extraction prosody contours 

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References

  1. 1.
    Dziubiński, M., Kostek, B.: High accuracy and octave error immune pitch detection algorithms. Archives of Acoustics 29(1), 1–21 (2004)Google Scholar
  2. 2.
    Gerhard, D.: Pitch Extraction and Fundamental Frequency: History and Current Techniques. Technical Report TR-CS 2003-06, Dept. of Computer Science, University of Regina (2003)Google Scholar
  3. 3.
    Paeschke, A.: Global Trend of Fundamental Frequency in Emotional Speech. In: Proceedings of Speech Prosody, Nara, Japan (2004)Google Scholar
  4. 4.
    Boersma, P.: Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In: IFA Proceedings 17 (1993)Google Scholar
  5. 5.
    Boersma, P.: Praat, a system for doing phonetics by computer. Glot International 5(9/10), 341–345 (2001)Google Scholar
  6. 6.
    Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W., Weiss, B.: A Database of German Emotional Speech. In: Proceedings Interspeech, Portugal (2005)Google Scholar
  7. 7.
    Ververidis, D., Kotropoulos, C.: Emotional speech recognition: Resources, features, and methods. Speech Communication 48(9) (2006)Google Scholar
  8. 8.
    Neiberg, D., Elenius, K., Karlsson, I., Laskowski, K.: Emotion Recognition in Spontaneous Speech. Working Papers 52, University of Lund (2006)Google Scholar
  9. 9.
    Niewiadomy, D., Pelikant, A.: Digital Speech Signal Parametrization by Mel Frequency Cepstral Coefficients and Word Boundaries. Journal of Applied Computer Science 15(2), 71–81 (2007)Google Scholar
  10. 10.
    Mao, X., Chen, L., Zhang, B.: Mandarin speech emotion recognition based on a hybrid HMM/ANN. International Journal of Computers 1(4) (2007)Google Scholar
  11. 11.
    Nogueiras, A., Moreno, A., Bonafonte, A., Mariño, J.B.: Speech Emotion Recognition Using Hidden Markov Models. In: 7th European Conference on Speech Communication and Technology, Aalborg, Denmark (2001)Google Scholar
  12. 12.
    Mansoorizadeh, M., Charkari, N.M.: Speech emotion recognition: comparison of speech segmentation approaches. In: IKT 2007 (2007)Google Scholar
  13. 13.
    Datcu, D., Rothkrantz, L.J.M.: The recognition of emotions from speech using GentleBoost classifier. A comparison approach. In: International Conference on Computer Systems and Technologies (2006)Google Scholar
  14. 14.
    Koolagudi, S.G., Rao, K.S.: Real life emotion classification using VOP and pitch based spectral features. In: India Conference (INDICON) Annual IEEE (2010)Google Scholar
  15. 15.
    Prasanna, S.R.M., Reddy, B.V.S., Krishnamoorthy, P.: Vowel onset point detection using source, spectral peaks, and modulation spectrum energies. IEEE Trans. Audio, Speech, and Language Processing 17, 556–565 (2009)CrossRefGoogle Scholar
  16. 16.
    Murty, K.S.R., Yegnanarayana, B.: Epoch extraction from speech signals. IEEE Trans. Audio, Speech, Language Processing 16(8), 1602–1615 (2008)CrossRefGoogle Scholar
  17. 17.
    Hahn, M., Kang, D.G.: Precise glottal closure instant detector for voiced speech. IEE Electronics Letters 32(23) (1996)Google Scholar
  18. 18.
    Shami, M.T., Kamel, M.S.: Segment-based approach to the recognition of emotions in speech. In: ICME (2005)Google Scholar
  19. 19.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011)Google Scholar
  20. 20.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)Google Scholar
  21. 21.
    Xuedong, H., Acero, A., Hon, H.W.: Spoken Language Processing. Prentice Hall PTR (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bartłomiej Stasiak
    • 1
  • Krzysztof Rychlicki-Kicior
    • 1
  1. 1.Institute of Information TechnologyTechnical University of ŁódźŁódźPoland

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