Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

4 Issues per year

Open Access
Online
ISSN
2083-2567
See all formats and pricing
More options …

Multi-Objective Heuristic Feature Selection for Speech-Based Multilingual Emotion Recognition

Christina Brester
  • Corresponding author
  • Institute of Computer Science and Telecommunications, Reshetnev Siberian State Aerospace University, Krasnoyarsky rabochy Av. 31, 660037, Krasnoyarsk, Russian Federation
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Eugene Semenkin
  • Institute of Computer Science and Telecommunications, Reshetnev Siberian State Aerospace University, Krasnoyarsky rabochy Av. 31, 660037, Krasnoyarsk, Russian Federation
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Maxim Sidorov
  • Institute of Communications Engineering, Ulm University, Albert Einstein-Allee 43, 89081, Ulm, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-08-10 | DOI: https://doi.org/10.1515/jaiscr-2016-0018

Abstract

If conventional feature selection methods do not show sufficient effectiveness, alternative algorithmic schemes might be used. In this paper we propose an evolutionary feature selection technique based on the two-criterion optimization model. To diminish the drawbacks of genetic algorithms, which are applied as optimizers, we design a parallel multicriteria heuristic procedure based on an island model. The performance of the proposed approach was investigated on the Speech-based Emotion Recognition Problem, which reflects one of the most essential points in the sphere of human-machine communications. A number of multilingual corpora (German, English and Japanese) were involved in the experiments. According to the results obtained, a high level of emotion recognition was achieved (up to a 12.97% relative improvement compared with the best F-score value on the full set of attributes).

Keywords: multi-objective optimization; feature selection; speech-based emotion recognition

References

  • [1] R. Kohavi, G.H. John, Wrappers for feature subset selection. Artificial Intelligence, 97, pp. 273-324, 1997.Google Scholar

  • [2] S.B. Thrun, The Monk’s problems: a performance comparison of different learning algorithms, Tech. Rept. CMU-CS-91-197, Carnegie Mellon University, Pittsburgh, PA, 1991.Google Scholar

  • [3] G.H. John, Enhancements to the data mining process. Ph.D. Thesis, Computer Science Department, Stanford University, CA, 1997.Google Scholar

  • [4] M. Venkatadri, K. Srinivasa Rao, A multiobjective genetic algorithm for feature selection in data mining, International Journal of Computer Science and Information Technologies, vol. 1, no. 5, 2010, pp. 443-448.Google Scholar

  • [5] Ch. Brester, M. Sidorov, E. Semenkin, Acoustic Emotion Recognition: TwoWays of Feature Selection Based on Self-Adaptive Multi-Objective Genetic Algorithm, Proceedings of the International Conference on Informatics in Control, Automation and Robotics (ICINCO), 2014, pp. 851-855.Google Scholar

  • [6] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation 6 (2), 2002, pp. 182-197.CrossrefGoogle Scholar

  • [7] R. Wang, Preference-Inspired Co-evolutionary Algorithms, A thesis submitted in partial fulfillment for the degree of the Doctor of Philosophy, University of Sheffield, 2013, p. 231.Google Scholar

  • [8] E. Zitzler, M. Laumanns, L. Thiele, SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization, Evolutionary Methods for Design Optimisation and Control with Application to Industrial Problems EUROGEN 2001 3242 (103), 2002, pp. 95-100.Google Scholar

  • [9] D. Whitley, S. Rana, and R. Heckendorn, Island model genetic algorithms and linearly separable problems, Proceedings of AISBWorkshop on Evolutionary Computation, Manchester, UK. Springer, volume 1305 of LNCS, 1997, pp. 109-125.Google Scholar

  • [10] Ch. Brester, E. Semenkin, Cooperative Multiobjective Genetic Algorithm with Parallel Implementation // Advances in Swarm and Computational Intelligence, LNCS 9140, 2015, pp. 471-478.Google Scholar

  • [11] R.W. Picard, Affective computing. Tech. Rep. Perceptual Computing Section Technical Report No. 321, MIT Media Laboratory, 20 Ames St., Cambridge, MA 02139, 1995.Google Scholar

  • [12] P. Boersma, Praat, a system for doing phonetics by computer, Glot international, vol. 5, no. 9/10, 2002, pp. 341-345.Google Scholar

  • [13] F. Eyben, M. Wllmer, and B. Schuller, Opensmile: the Munich versatile and fast opensource audio feature extractor, Proceedings of the international conference on Multimedia, 2010. ACM, pp. 1459-1462.Google Scholar

  • [14] F. Burkhardt, A. Paeschke, M. Rolfes, W. F. Sendlmeier, and B. Weiss, A database of german emotional speech, In Interspeech, 2005, pp. 1517-1520.Google Scholar

  • [15] S. Haq, P. Jackson, Machine Audition: Principles, Algorithms and Systems, chapter Multimodal Emotion Recognition, IGI Global, Hershey PA, Aug. 2010, pp. 398-423.Google Scholar

  • [16] A. Schmitt, S. Ultes, and W. Minker, A parameterized and annotated corpus of the cmu let’s go bus information system, Proceedings of International Conference on Language Resources and Evaluation (LREC), 2012.Google Scholar

  • [17] H. Mori, T. Satake, M. Nakamura, and H. Kasuya, Constructing a spoken dialogue corpus for studying paralinguistic information in expressive conversation and analyzing its statistical/acoustic characteristics, Speech Communication, 53, 2011.Web of ScienceGoogle Scholar

  • [18] Ch. Brester, M. Sidorov, E. Semenkin, Speechbased emotion recognition: Application of collective decision making concepts, Proceedings of the 2nd International Conference on Computer Science and Artificial Intelligence (ICCSAI2014), 2014, pp. 216-220.Google Scholar

  • [19] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I. H.Witten, The WEKA Data Mining Software: An Update, SIGKDD Explorations, Vol. 11, Issue 1, 2009.Google Scholar

  • [20] C. Goutte, E. Gaussier, A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. ECIR’05 Proceedings of the 27th European conference on Advances in Information Retrieval Research, 2005, pp. 345-359.Google Scholar

About the article

Published Online: 2016-08-10

Published in Print: 2016-10-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, Volume 6, Issue 4, Pages 243–253, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2016-0018.

Export Citation

© 2016. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Vladimir Stanovov, Christina Brester, Mikko Kolehmainen, and Olga Semenkina
IOP Conference Series: Materials Science and Engineering, 2017, Volume 173, Page 012020

Comments (0)

Please log in or register to comment.
Log in