Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Robust Speech Recognition using Vocal Tract Normalization for Emotional Variation

성도 정규화를 이용한 감정 변화에 강인한 음성 인식

  • 김원구 (군산대학교 전기공학과) ;
  • 방현진 (군산대학교 컴퓨터정보공학과)
  • Received : 2009.06.29
  • Accepted : 2009.12.01
  • Published : 2009.12.25
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Abstract

This paper studied the training methods less affected by the emotional variation for the development of the robust speech recognition system. For this purpose, the effect of emotional variations on the speech signal were studied using speech database containing various emotions. The performance of the speech recognition system trained by using the speech signal containing no emotion is deteriorated if the test speech signal contains the emotions because of the emotional difference between the test and training data. In this study, it is observed that vocal tract length of the speaker is affected by the emotional variation and this effect is one of the reasons that makes the performance of the speech recognition system worse. In this paper, vocal tract normalization method is used to develop the robust speech recognition system for emotional variations. Experimental results from the isolated word recognition using HMM showed that the vocal tract normalization method reduced the error rate of the conventional recognition system by 41.9% when emotional test data was used.

본 논문에서는 인간의 감정 변화에 강인한 음성 인식 시스템을 구현하기 위하여 감정 변화의 영향을 최소화 하는 방법에 관한 연구를 수행하였다. 이를 위하여 우선 다양한 감정이 포함된 음성 데이터베이스를 사용하여 감정 변화에 따른 음성 신호의 변화를 관찰하였다. 감정이 포함되지 않은 평상의 음성으로 학습된 음성 인식 시스템에 감정이 포함된 인식 데이터가 입력되는 경우 감정에 따른 음성의 차이가 인식 시스템의 성능을 저하시킨다. 본 연구에서는 감정의 변화에 따라 화자의 성도 길이가 변화한다는 것과 이러한 변화는 음성 인식 시스템의 성능을 저하시키는 원인 중의 하나임을 관찰하였다. 본 연구에서는 이러한 음성의 변화를 감소시키는 방법으로 성도 길이 정규화 방법을 사용한 감정 변화에 강인한 음성 인식 시스템을 개발하였다. HMM을 사용한 단독음 인식 실험에서 제안된 학습 방법을 사용하면 감정 데이터의 오차가 기존방법보다 41.9% 감소되었다.

Keywords

References

  1. J. C. Junqua, and J. P. Haton, Robustness in Automatic Speech Recognition - Fundamental and Applications, Kluwer Academic Publishers, 1996
  2. A. Acero and R. M. Stern, 'Environmental robustness in automatic speech recognition,' in Proceedings of ICASSP, pp. 849-852, April 1990
  3. H. Hermansky, N. Morgan, H. G. Hirsch, 'Recognition of speech in additive and convolutional noise based RASTA spectral processing', in Proceedings of ICASSP, pp. 83-86, 1993
  4. J. Koehler, N. Morgan, H. Hermansky, H. G. Hirsch, G. Tong, 'Integrating RASTA-PLP into Speech Recognition', in Proc. ICASSP, pp. 421-424, 1994
  5. H. Hermansky, N. Morgan, A. Bayya, P. Kohn, 'Compensation for the effect of the communication channel in auditory-like analysis of speech (RASTA-PLP)', in Proceedings of EUROSPEECH, vol. 3, pp. 1367-1370, 1991
  6. P. Alexandre, P. Lockwood, 'Root cepstral analysis: a unified view. application to speech processing in car noise environments', Speech Communication, vol. 12, no. 3, pp. 277-288, 1993 https://doi.org/10.1016/0167-6393(93)90099-7
  7. M. G. Rahim, B. H. Juang, 'Signal bias removal by maximum likelihood estimation for robust telephone speech recognition', IEEE Trans. Speech & Audio Processing, vol. 4, No. 1, pp. 19-30, 1996 https://doi.org/10.1109/TSA.1996.481449
  8. N. Amir, 'Classifying emotions in speech: a comparison of methods', in Proceedings of Eurospeech '2001, Vol. 1, pp. 127-130, Aalborg, Denmark, 2001
  9. A. Nogueiras, etc, 'Speech emotion recognition using Hidden Markov Models', in Proceedings of Eurospeech '2001, Vol. 4, pp. 2679-2682, Aalborg, Denmark, 2001
  10. R. W. Picard, Affective Computing, The MIT Press 1997
  11. J. E. Cahn, 'The generation of affect in synthesized speech', Journal of the American Voice I/O Society, Vol. 8, pp. 1-19, July 1990
  12. K. R. Scherer, D. R. Ladd, and K. E. A. Silverman, 'Vocal cues to speaker affect: testing two models', Journal Acoustical Society of America, Vol. 76, No. 5, pp. 1346-1355, Nov. 1984 https://doi.org/10.1121/1.391450
  13. I. R. Murray and J. L. Arnott, 'Toward the simulation of emotion in synthetic speech: a review of the literature on human vocal emotion', Journal of Accoustal Society of America., pp. 1097-1108, Feb. 1993
  14. C. E. Williams and K. N. Stevens, 'Emotions and speech: some acoustical correlates', Journal Acoustical Society of America, Vol. 52, No. 4, pp. 1238-1250, 1972 https://doi.org/10.1121/1.1913238
  15. M. Lewis and J. M. Haviland, Handbook of Emotions, The Guilford Press 1993
  16. F. Dellaert, T. Polzin, A. Waibel, 'Recognizing emotion in speech', in Proceedings of the ICSLP 96, Philadelphia, USA, Oct. 1996
  17. J. Sato, and S. Morishima, 'Emotion modeling in speech production using emotion space', in Proceedings of the IEEE International Workshop 1996, pp. 472-477, Piscataway, NJ, USA., 1996
  18. T. S. Huang, L. S. Chen and H. Tao, 'Bimodal emotion recognition by man and machine', in ATR Workshop on Virtual Communication Environments-Bridges over Art/Kansei and VR Technologies, Kyoto, Japan, 1998
  19. T. S. Polzin and A. H. Waibel, 'Detecting emotions in speech', Proceedings of the CMC (Cooperative Multimodal Communication), 1998
  20. J. Vroomen, R. Collier and S. Mozziconacci, 'Duration and intonation in emotional Speech', in Proceedings of Eurospeech '93, Vol.1, pp.577-580, Berlin, Germany, 1993
  21. B. Heuft, T. Portele, M. Rauth, 'Emotions in time domain synthesis', in Proceedings of ICSLP '96, Vol. 3, pp.1974-1977, Philadelphia, PA, USA, 1996
  22. M. Pitz, H. Ney, 'Vocal tract normalization equals linear transformation in cepstral space', IEEE Trans. Speech & Audio Processing, vol. 13, No. 5, pp. 930-944, 2005 https://doi.org/10.1109/TSA.2005.848881
  23. S. Wegmann, D. McAllaster, J. Orlofl and B. Peskin, 'Speaker Normalization on Conversational Telephone Speech, in Proceedings of ICASSP, Atlanta, GA, pp. 339-342, May 1996
  24. L. Welling, R. Haeb-Umbach, X. Aubert and N. Haberland, 'A study on speaker Normalization using vocal tract normalization and speaker adaptive training', in Proceedings of ICASSP, Seattle, WA, pp. 797-800, May 1998
  25. A. Acero and R. M. Stern, 'Robust speech recognition by normalization of the acoustic space', in Proceedings of. ICASSP, Toronto, pp. 893-896, May 1991
  26. E. Eide and H. Gish, 'A parametric approach to vocal tract length normalization', in Proceedings of ICASSP, Atlanta, GA, pp.346-349, May 1996
  27. Sirko Molau, Stephan Kanthak , Hermann Ney, 'Efficient Vocal Tract Normalization in Automatic Speech Recognition', in Proceedings of of the ESSV'00, Cottbus, Germany, pp. 209-216, 2000
  28. 강봉석, '음성 신호를 이용한 문장독립 감정 인식 시스템', 연세대학교 석사학위 논문, 2000