전기학회논문지 (The Transactions of The Korean Institute of Electrical Engineers)

  • 제66권7호
  • /
  • Pages.1105-1110
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  • 2017
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  • 1975-8359(pISSN)
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  • 2287-4364(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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감정 적응을 이용한 감정 화자 인식

Emotional Speaker Recognition using Emotional Adaptation

  • Kim, Weon-Goo (Dept. of Electrical Engineering, Kunsan National University)
  • 투고 : 2017.06.01
  • 심사 : 2017.06.16
  • 발행 : 2017.07.01
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초록

Speech with various emotions degrades the performance of the speaker recognition system. In this paper, a speaker recognition method using emotional adaptation has been proposed to improve the performance of speaker recognition system using affective speech. For emotional adaptation, emotional speaker model was generated from speaker model without emotion using a small number of training affective speech and speaker adaptation method. Since it is not easy to obtain a sufficient affective speech for training from a speaker, it is very practical to use a small number of affective speeches in a real situation. The proposed method was evaluated using a Korean database containing four emotions. Experimental results show that the proposed method has better performance than conventional methods in speaker verification and speaker recognition.

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참고문헌

  1. K. R. Scherer, T. Johnstone, G. Klasmeyer, and T. Banziger, "Can Automatic Speaker Verification be Improved by Training the Algorithms on Emotional Speech?", ICSLP 2000, pp. 807-810, 2000.
  2. I.R. Murray, J.L. Arnott, "Applying an Analysis of Acted Vocal Emotions to Improve the Simulation of Synthetic Speech", Computer Speech and Language 22(2), pp. 107-129. 2008 https://doi.org/10.1016/j.csl.2007.06.001
  3. B. Huanjun, X. Mingxing, and F. Z. Thomas, "Emotion Attribute Projection for Speaker Recognition on Emotional Speech". EUROSPEECH 2007, Antwerp, pp. 758-761, 2007.
  4. K. Alluri, V. Raju, S. Gangashetty, A. Kumar Vuppala "Analysis of Source and System features for Speaker Recognition in Emotional Conditions", 2016 IEEE Region 10 Conference (TENCON), pp. 2847-2850, 2016
  5. L. Dongdong, Y. Yingchun, and W. Zhaohui, "Emotion-State Conversion for Speaker Recognition", ACII 2005, Beijing, pp. 403-410, 2005.
  6. W. Wei, F.Z. Thomas, M. X, and B. HuanJun, "Study on Speaker Verification on Emotional Speech", Proc. Interspeech '2006, pp. 2102-2105, 2006.
  7. L. Mackova, A. Cizmar, "Emotional Speaker Verification Based on I-vectors", 5th IEEE International Conference on Cognitive Infocommunications, pp. 533-536, 2014.
  8. W. Tian, Y. Yingchun, and W. Zhao-hui, "Improving Speaker Recognition by Training on Emotion-Added Models", ACII 2005, Beijing, pp. 382-389, 2005.
  9. L. Dongdong and Y. Yingchun, "Emotional Speech Clustering based Robust Speaker Recognition System," the 2nd International Congress on Image and Signal Processing, pp. 1-5, Tianjin, China, Oct. 2009.
  10. L. Dongdong and Y. Yingchun, D. Weihui, "Cost-Sensitive Learning for Emotion Robust Speaker Recognition", The Scientific World Journal, Vol. 2014, pp. 1-9, 2014.
  11. L. Chen, Y. Yang, "A preliminary study on GMM weight transformation for Emotional Speaker Recognition", 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 31-36, 2013
  12. Reynolds, D.A., Quatieri, T.F., Dunn, R.B., "Speaker Verifi cation using Adapted Gaussian Mixture Models", Digital Signal Processing 10, pp. 19-41, 2000. https://doi.org/10.1006/dspr.1999.0361