Emotion Recognition using Robust Speech Recognition System

강인한 음성 인식 시스템을 사용한 감정 인식

  • 김원구 (군산대학교 전자정보공학부)
  • Published : 2008.10.25


This paper studied the emotion recognition system combined with robust speech recognition system in order to improve the performance of emotion recognition system. For this purpose, the effect of emotional variation on the speech recognition system and robust feature parameters of speech recognition system were studied using speech database containing various emotions. Final emotion recognition is processed using the input utterance and its emotional model according to the result of speech recognition. In the experiment, robust speech recognition system is HMM based speaker independent word recognizer using RASTA mel-cepstral coefficient and its derivatives and cepstral mean subtraction(CMS) as a signal bias removal. Experimental results showed that emotion recognizer combined with speech recognition system showed better performance than emotion recognizer alone.


  1. Oh-Wook Kwon, etc "Emotion Recognition by Speech Signal", Proceedings of Eurospeech '2003, Vol. 1, pp. 125-128, Geneva, 2003
  2. K. R. Scherer, "Adding the Affective dimension : A New Look in Speech Analysis and Synthesis", Proceedings of ICSLP, 2002
  3. Noam Amir,"Classifying Emotions in Speech: a Comparison of Methods", Proceedings of Eurospeech '2001, Vol. 1, pp. 127-130, Aalborg, Denmark, 2001
  4. A. Nogueiras, etc,"Speech Emotion Recognition using Hidden Markov Models", Proceedings of Eurospeech '2001, Vol. 4, pp. 2679-2682, Aalborg, Denmark, 2001
  5. Rosalind W. Picard, Affective Computing, The MIT Press 1997
  6. Janet E. Cahn, "The Generation of Affect in Synthesized Speech", Journal of the American Voice I/O Society, Vol. 8, pp. 1-19, July 1990
  7. 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
  8. Iain R. Murray and John 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
  9. 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
  10. Michael Lewis and Jeannette M. Haviland, Handbook of Emotions, The Guilford Press 1993
  11. Rainer Banse and Klaus R. Scherer, "Acoustic Profiles in Vocal Emotion Expression", Journal of Personality and Social Psychology, Vol. 70, No. 3, pp. 614-636, 1996
  12. Frank Dellaert, Thomas Polzin, Alex Waibel, "Recognizing Emotion in Speech", Proceedings of the ICSLP 96, Philadelphia, USA, Oct. 1996
  13. Jun Sato, and Shigeo Morishima, "Emotion Modeling in Speech Production using Emotion Space", Proceedings of the IEEE International Workshop 1996, pp. 472-477, IEEE, Piscataway, NJ, USA., 1996
  14. Thomas S. Huang, Lawrence S. Chen and Hai Tao, "Bimodal Emotion Recognition by Man and Machine", ATR Workshop on Virtual Communication Environments-Bridges over Art/Kansei and VR Technologies, Kyoto, Japan, April 1998
  15. J. Koehler, N. Morgan, H. Hermansky, H. G. Hirsch, G. Tong, "Integrating RASTA-PLP into Speech Recognition", in Proc. ICASSP, pp. 421-424, 1994
  16. 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 Proc. EUROSPEECH, vol.3, pp. 1367-1370, Sep. 1991
  17. P.Alexandre, ect. "Root Cepstral Analysis: A Unified View. Application to Speech Processing in Car Noise Environments", Speech Communication, vol. 12, no. 3, pp. 277-288, 1993
  18. 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
  19. L. R. Rabiner and B. H. Juang, Fundamentals of speech recognition, Prentice-Hall Inc., 1993