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On the Importance of Tonal Features for Speech Emotion Recognition

음성 감정인식에서의 톤 정보의 중요성 연구

  • 이정인 (연세대학교 전기전자공학과) ;
  • 강홍구 (연세대학교 전기전자공학과)
  • Received : 2013.06.25
  • Accepted : 2013.08.22
  • Published : 2013.09.30

Abstract

This paper describes an efficiency of chroma based tonal features for speech emotion recognition. As the tonality caused by major or minor keys affects to the perception of musical mood, so the speech tonality affects the perception of the emotional states of spoken utterances. In order to justify this assertion with respect to tonality and emotion, subjective hearing tests are carried out by using synthesized signals generated from chroma features, and consequently show that the tonality contributes especially to the perception of the negative emotion such as anger and sad. In automatic emotion recognition tests, the modified chroma-based tonal features are shown to produce noticeable improvement of accuracy when they are supplemented to the conventional log-frequency power coefficient (LFPC)-based spectral features.

본 연구는 음성의 감정인식에 있어서 크로마 피쳐를 기반으로 한 음성 토널 특성에 대하여 기술하였다. 토널 정보가 갖는 장조와 단조와 같은 정보가 음악의 분위기에 미치는 영향과 유사하게 음성의 감정을 인지하는 데에도 토널 정보의 영향이 존재한다. 감정과 토널 정보의 관계를 분석하기 위해서, 본 연구에서는 크로마 피쳐로부터 재합성된 신호를 이용하여 청각 실험을 수행하였고, 인지실험결과 긍정과 부정적 감정에 대한 구분이 가능한 것으로 확인되었다. 인지 실험을 바탕으로 음성에 적합한 토널 피쳐를 적용하여 감정인식 실험을 진행하였고, 토널 피쳐를 사용하였을 경우 감정인식 성능이 향상되는 것을 확인 할 수 있다.

Keywords

References

  1. R. Cowie, E. Douglas-Cowei, N. Tsapatsoulis, G. Votsis, S. Kollias, W. Fellenz, and J. G. Taylor, "Emotion Recognition in Human Computer Interaction," IEEE Signal Processing Magazine, pp. 32-80, 2001.
  2. D. Ververidis and C. Kotropoulos, "Emotional speech recognition: Resources, features, and methods," Speech Communication, vol. 48(9), pp. 1162-1181, 2006. https://doi.org/10.1016/j.specom.2006.04.003
  3. M. E. Ayadi, M. S. Kamel, and F. Karray, "Survey on speech emotion recognition: Features, classification schemes, and databases", Pattern Recognition, vol. 44, pp. 572-587, 2011. https://doi.org/10.1016/j.patcog.2010.09.020
  4. I. Murray, J. Arnott, "Toward the simulation of emotion in synthetic speech: A review of the literature of human vocal emotion," J. Acoust. Soc. Am, vol. 93 (2), pp. 1097-1108, 1993. https://doi.org/10.1121/1.405558
  5. C. E. Williams and K. N. Stevens, "Emotion and speech: Some acoustical correlates", J. Acoust. Soc. Am, vol. 52(4), pp. 1238-1250, 1972. https://doi.org/10.1121/1.1913238
  6. C. Gobl and A. N. Chasaide, "The role of voice quality in communicating emotion, mood and attitude", Speech Communication, vol. 40, pp. 189-212, 2003. https://doi.org/10.1016/S0167-6393(02)00082-1
  7. M. Goudbeek and K. Scherer, "Beyond arousal: Valence and potency/ control cues in the vocal expression of emotion", J. Acoust. Soc. Am, vol. 128, pp. 1322-1336, 2010. https://doi.org/10.1121/1.3466853
  8. S. Yacoub, S. Simske, X. Lin, J. Burns, "Recognition of Emotionsin Interactive Voice Response System," Proceedings of the Eurospeech 2003, Geneva, 2003.
  9. T. L. Nwe, S. W. Foo, and et al, "Speech emotion recognition using hidden markov models", Speech Communication, vol. 41(4), pp. 603-623, 2003. https://doi.org/10.1016/S0167-6393(03)00099-2
  10. M. A. Bartsch and G. H. Wakefield, "Audio thumbnailing of popular music using chroma-based representation," IEEE Transactions on Multimedia, vol. 7(1), pp. 96-104, 2005. https://doi.org/10.1109/TMM.2004.840597
  11. Y. E. Kim, E. M. Schmidt, R. Migneco, B. G. Morton, P. Richardson, J. Scott, J. A. Speck, and D. Turnbull, "Music emotion recognition: A state of the art review", Proc. 11th Int. Soc. Music Information Retrieval Conf.(ISMIR), pp. 255-266, 2010.
  12. M. Muller, F. Kurth, and M. Clausen, "Audio matching via chroma- based statistical features", Proc. 5th Int. Soc. Music Information Retrieval Conf.(ISMIR), pp. 288-295, 2005.
  13. T. F. Quatieri, Discrete-Time Speech Signal Processing, Prentice-Hall, NJ, 2002.
  14. H. Purwins, "Profiles of Pitch Classes: Circularity of Relative Pitch and Key: Experiments, Models, Computational Music Analysis, and Perspectives," Ph. D. dissertation, Berlin Univ. of Technol., Berlin, Germany, 2005.
  15. T. Lan, D. Erdogmus, U. Ozertem, and Y. Huang, "Estimating mutual information using Gaussian mixture model for feature ranking and selection", Proc. Int. Joint Conf. on Neural Networks, pp. 5034-5039, 2006.
  16. B.-S. Kang, "Text-independent emotion recognition algorithm using speech signal," M. S. Thesis, Yonsei university, Electrical and Electronic Engineering Department, 2000.
  17. C.-C. Chang and C.-J. Lin, "Libsvm: a library for support vector machines", ACM Transactions on Intelligent Systems and Technology, vol. 2, 27:1-26, 2011.
  18. P. Shen, Z. Changjun, X. CHen, "Automatic Speech Emotion Recognition using Support Vector Machine," Int. Conf. on Electronic and Mechanical Engineering and Information Technology (EMEIT), vol. 2, pp. 621-625, 2011.