[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Improving Noise Robustness of Speech Emotion Recognition System

  • Conference paper
Intelligent Distributed Computing VII

Part of the book series: Studies in Computational Intelligence ((SCI,volume 511))

Abstract

In this paper method of improving noise robustness of speech emotion recognition system is proposed. Such a system has been developed for use in a social robot, but its performance is highly degraded by environmental noise. To alleviate this problem, the histogram equalisation is proposed to reduce the difference between feature vectors in clean and noisy conditions. In training phase of the system the averaged histograms of pitch and MFCC are computed and then serve as reference for equalisation. System performance was evaluated using Database of Polish Emotional Speech, which was split into training and test sets. Test sets were noised with 3 different noise samples. Presented preliminary results show a significant improvement of recognition accuracy in noisy environment conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 143.50
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 179.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
GBP 179.99
Price includes VAT (United Kingdom)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Enhance contrast using histogram equalization, http://www.mathworks.com/help/images/ref/histeq.html

  2. Robot FLASH, http://lirec.ict.pwr.wroc.pl

  3. Alam, M.J., Ouellet, P., Kenny, P., O’Shaughnessy, D.: Comparative evaluation of feature normalization techniques for speaker verification. In: Travieso-González, C.M., Alonso-Hernández, J.B. (eds.) NOLISP 2011. LNCS (LNAI), vol. 7015, pp. 246–253. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  4. Casale, S., Russo, A., Scebba, G., Serrano, S.: Speech emotion classification using machine learning algorithms. In: Proceedings of the 2008 IEEE International Conference on Semantic Computing, pp. 158–165. IEEE Computer Society, Washington, DC (2008), doi:10.1109/ICSC.2008.43

    Chapter  Google Scholar 

  5. Dautenhahn, K.: Creating emotion recognition agents for speech signal. Multiagent systems, artificial societies, and simulated organizations. In: Socially Intelligent Agents: Creating Relationships with Computers and Robots. Kluwer Academic Publishers (2002)

    Google Scholar 

  6. García, L., Segura, J.C., de la Torre, Á., Benítez, C., Rubio, A.: Histogram equalization for robust speech recognition (2008), http://www.intechopen.com/books/speech_recognition/histogram_equalization_for_robust_speech_recognition

  7. Georgogiannis, A., Digalakis, V.: Speech emotion recognition using non-linear teager energy based features in noisy environments. In: 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pp. 2045–2049 (2012)

    Google Scholar 

  8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11, 10–18 (2009), doi: http://doi.acm.org/10.1145/1656274.1656278

  9. Hilger, F., Ney, H.: Quantile based histogram equalization for noise robust large vocabulary speech recognition. IEEE Transactions on Audio, Speech, and Language Processing 14(3), 845–854 (2006)

    Article  Google Scholar 

  10. Juszkiewicz, Ł.: Speech emotion recognition system for social robots. In: Postępy Robotyki, pp. 695–704. Oficyna wydawnicza PW (2012) (in Polish)

    Google Scholar 

  11. Lodz University of Technology, Medical Electronics Division: Database of Polish Emotional Speech, http://www.eletel.p.lodz.pl/bronakowski/med_catalog/docs/licence.txt

  12. Mao, X., Zhang, B., Luo, Y.: Speech emotion recognition based on a hybrid of HMM/ANN. In: Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications, vol. 7, pp. 367–370. World Scientific and Engineering Academy and Society (WSEAS), Stevens Point (2007)

    Google Scholar 

  13. Neiberg, D., Elenius, K.: Automatic recognition of anger in spontaneous speech

    Google Scholar 

  14. Nwe, T.L., Foo, S.W., Silva, L.C.D.: Speech emotion recognition using hidden markov models. Speech Communication 41, 603–623 (2003), doi:10.1016/S0167-6393(03)00099-2

    Article  Google Scholar 

  15. Paul Boersma, D.W.: Praat: doing phonetics by computer, version 5.2.05 (2010), http://www.praat.org/

  16. Peebles, P.Z.: Probability, random variables, and random signal principles / Peyton Z. Peebles Jr., 3rd edn. McGraw-Hill, New York (1993)

    Google Scholar 

  17. Schuller, B., Arsic, D., Wallhoff, F., Rigoll, G.: Emotion recognition in the noise applying large acoustic feature sets. In: Speech Prosody (2006)

    Google Scholar 

  18. Schuller, B., Seppi, D., Batliner, A., Maier, A., Steidl, S.: Towards more reality in the recognition of emotional speech. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2007, vol. 4, pp. IV-941–IV-944 (April 2007)

    Google Scholar 

  19. Schuller, B.W.: Speaker, noise, and acoustic space adaptation for emotion recognition in the automotive environment. In: 2008 ITG Conference on Voice Communication (SprachKommunikation), pp. 1–4 (2008)

    Google Scholar 

  20. Sidorova, J.: Speech emotion recognition with TGI+.2 classifier. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, EACL 2009, pp. 54–60. Association for Computational Linguistics (2009)

    Google Scholar 

  21. Tawari, A., Trivedi, M.: Speech emotion analysis in noisy real-world environment. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 4605–4608 (2010)

    Google Scholar 

  22. Viikki, O., Laurila, K.: Cepstral domain segmental feature vector normalization for noise robust speech recognition. Speech Communication 25(1-3), 133–147 (1998), doi:10.1016/S0167-6393(98)00033-8

    Article  Google Scholar 

  23. Xiao, Z., Dellandréa, E., Dou, W., Chen, L.: Hierarchical Classification of Emotional Speech. Tech. Rep. RR-LIRIS-2007-006, LIRIS UMR 5205 CNRS/INSA de Lyon/Université Claude Bernard Lyon 1/Université Lumiére Lyon 2/École Centrale de Lyon (2007)

    Google Scholar 

  24. Zhou, J., Wang, G., Yang, Y., Chen, P.: Speech emotion recognition based on rough set and SVM. In: Yao, Y., Shi, Z., Wang, Y., Kinsner, W. (eds.) IEEE ICCI, pp. 53–61. IEEE (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Łukasz Juszkiewicz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Juszkiewicz, Ł. (2014). Improving Noise Robustness of Speech Emotion Recognition System. In: Zavoral, F., Jung, J., Badica, C. (eds) Intelligent Distributed Computing VII. Studies in Computational Intelligence, vol 511. Springer, Cham. https://doi.org/10.1007/978-3-319-01571-2_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-01571-2_27

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01570-5

  • Online ISBN: 978-3-319-01571-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics