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International Symposium on Neural Networks

ISNN 2016: Advances in Neural Networks – ISNN 2016 pp 613–619Cite as

New Adaptive Feature Vector Construction Procedure for Speaker Emotion Recognition Based on Wavelet Transform and Genetic Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9719))

Abstract

In this paper new original method of adaptive feature vector construction based on wavelet transform and genetic algorithm is proposed. Wavelet-based original feature vector is designed using genetic algorithm and support vector machine as classifier in order to provide better speaker emotion recognition. It was shown that the usage of the proposed adaptive feature vector lets to improve emotion recognition accuracy.

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References

  1. Akima, H.: A new method of interpolation and smooth curve fitting based on local procedures. J. ACM 17, 589–602 (1970)

    Article  MATH  Google Scholar 

  2. Back, T.: Evolution Algorithms in Theory and Practice. Oxford University Press, Oxford (1996)

    MATH  Google Scholar 

  3. Bellman, R.: Adaptive Control Processes Guided Tour. Princeton University Press, Princeton (1961)

    Book  MATH  Google Scholar 

  4. Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W., Weiss, B.: A database of german emotional speech. In: Proceedings of Interspeech, Lissabon, pp. 1517–1520 (2005)

    Google Scholar 

  5. Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Boston (1989)

    MATH  Google Scholar 

  6. Huang, C.L., Wang, C.J.: A GA-based feature selection and parameters optimization for support vector machines. Expert Syst. Appl. 31(2), 231–240 (2006)

    Article  Google Scholar 

  7. Huang, X., Acero, A., Hon, H.W.: Spoken Language Processing: A Guide to Theory, Algorithm, and System Development. Prentice Hall, Upper Saddle River (2001)

    Google Scholar 

  8. Kiranyaz, S., Raitoharju, J., Gabbouj, M.: Evolutionary feature synthesis forcontent-based audio retrieval. In: 2013 1st International Conference on Communications, Signal Processing, andtheir Applications (ICCSPA), pp. 1–6, February 2013

    Google Scholar 

  9. Kovalets, P., AlexanderSoroka: k52: v0.1 (2016). http://dx.doi.org/10.5281/zenodo.49181

  10. Lixia, H., Evangelista, G., Zhang, X.: Adaptive bands filter bank optimized by genetic algorithm for robust speech recognition system. J. Cent. South Univ. Technol. 18, 1595–1601 (2011)

    Article  Google Scholar 

  11. Mallat, S.: A Wavelet Tour of Signal Processing: The Sparse Way. Academic Press, San Diego (2008)

    MATH  Google Scholar 

  12. Murthy, D.S., Holla, N.: Robust speech recognition system designed by combining empirical mode decomposition and a genetic algorithm. Int. J. Eng. Res. Technol. (IJERT) 2, 2056–2068 (2013)

    Google Scholar 

  13. Schuller, B., Reiter, S., Rigoll, G.: Evolutionary feature generation in speech emotion recognition. In: 2006 IEEE International Conference on Multimedia and Expo, pp. 5–8 (2006)

    Google Scholar 

  14. Soroka, A., Kovalets, P., Kheidorov, I.: New method of speech signals adaptive features construction based on the wavelet-like transform and support vector machines. In: Ronzhin, A., Potapova, R., Delic, V. (eds.) SPECOM 2014. LNCS, vol. 8773, pp. 308–314. Springer, Heidelberg (2014)

    Google Scholar 

  15. Srinivasan, V., Ramalingam, V., Sellam, V.: Classification of normal and pathological voice using GA and SVM. Int. J. Comput. Appl. 60, 34–39 (2012)

    Google Scholar 

  16. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2000)

    Book  MATH  Google Scholar 

  17. Vignolo, L.D., Rufiner, H.L., Milone, D.H., Goddard, J.C.: Evolutionary splines for cepstral filterbank optimization in phoneme classification. EURASIP J. Adv. Signal Process. 2011, 1–14 (2011)

    Article  Google Scholar 

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Acknowledgement

Authors acknowledge financial support from Belarusian Republican Foundation for Fundamental Research, project F14-052.

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Authors and Affiliations

  1. Department of Radiophysics and Digital Media Technologies, Belarusian State University, Minsk, Belarus

    Alexander M. Soroka, Pavel E. Kovalets & Igor E. Kheidorov

Authors
  1. Alexander M. Soroka
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  2. Pavel E. Kovalets
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  3. Igor E. Kheidorov
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Corresponding author

Correspondence to Alexander M. Soroka .

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Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. Huazhong University of Science and Tech., Wuhan, Jiangsu, China

    Qingshan Liu

  3. Russian Academy of Sciences, SPIIRAS, St. Petersburg, Russia

    Andrey Ronzhin

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© 2016 Springer International Publishing Switzerland

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Soroka, A.M., Kovalets, P.E., Kheidorov, I.E. (2016). New Adaptive Feature Vector Construction Procedure for Speaker Emotion Recognition Based on Wavelet Transform and Genetic Algorithm. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_70

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  • DOI: https://doi.org/10.1007/978-3-319-40663-3_70

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40662-6

  • Online ISBN: 978-3-319-40663-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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