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

The Korean Institute of Electrical Engineers (대한전기학회)
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A Comparison of Effective Feature Vectors for Speech Emotion Recognition

음성신호기반의 감정인식의 특징 벡터 비교

  • Shin, Bo-Ra (Dept. of Computer Science, SangMyung University) ;
  • Lee, Soek-Pil (Dept. of Electronic Engineering, SangMyung University)
  • Received : 2018.07.20
  • Accepted : 2018.08.31
  • Published : 2018.10.01
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Abstract

Speech emotion recognition, which aims to classify speaker's emotional states through speech signals, is one of the essential tasks for making Human-machine interaction (HMI) more natural and realistic. Voice expressions are one of the main information channels in interpersonal communication. However, existing speech emotion recognition technology has not achieved satisfactory performances, probably because of the lack of effective emotion-related features. This paper provides a survey on various features used for speech emotional recognition and discusses which features or which combinations of the features are valuable and meaningful for the emotional recognition classification. The main aim of this paper is to discuss and compare various approaches used for feature extraction and to propose a basis for extracting useful features in order to improve SER performance.

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References

  1. Xu Huahu, Gao Jue and Yuan Jian, "Application of speech emotion recognition in intelligent household robot", inroceedings-International Conference on Artificial Intelligence and Computational Intelligence, AICI 2010, Vol. 1, 2010, pp. 537-541.
  2. Laurence Vidrascu and Laurence Devillers, "Detection of reallife emotions in call centers", in Interspeech 2005, 2005, pp. 1841-1844.
  3. Yigon Kim and Yong-Chel Bae, 2000, "Design of Emotion Recognition Model Using Fuzzy Logic", Journal of Korean Institute of Intelligent Systems, Vol. 10, No. 1, pp. 268-282.
  4. Jae Hun Bang and Sungyoung Lee, 2014, "Call Speech Emotion Recognition for Emotion based Services", Journal of KISS : Software and Applications, Vol. 41, No. 3, pp. 208-213.
  5. Byungwook Jung, Seungpyo Cheun, Yountae Kim and Sungshin Kim, 2008, "An Emotion Recognition Technique using Speech Signals", Journal of Korean Institute of Intelligent Systems, Vol. 18, No. 4, pp. 494-500. https://doi.org/10.5391/JKIIS.2008.18.4.494
  6. Seok-Pil Lee, Sang-Hui Park, Jeong-Seop Kim, Ig-Jae Kim "EMG pattern recognition based on evidence accumulation for prosthesis control", Proc Ann Intl Conf IEEE Eng Med Biol 4, pp. 1481-1483, 1996.
  7. Sung-Woo Byun and Seok-Pil Lee, 2016, "Emotion Recognition Using Tone and Tempo Based on Voice for IoT", The transactions of The Korean Institute of Electrical Engineers, Vol. 65, No. 1, pp. 116-121. https://doi.org/10.5370/KIEE.2016.65.1.116
  8. T.-L. Pao, Y.-T. Chen, J.-H. Yeh, and P.-J. Li, "Mandarin emotional speech recognition based on SVM and NN," Proc. of the 18th Int'l Conf. on Pattern Recognition (ICPR), Washington, DC, pp. 1096-1100, Aug 2006.
  9. Choi, Young Ho, Ban, Sung Min, Kim, Kyung-Wha and Kim, Hyung Soon, 2015, "Evaluation of Frequency Warping Based Features and Spectro-Temporal Features for Speaker Recognition", Phonetics and Speech Sciences, Vol. 7, No. 1, pp. 3-10. https://doi.org/10.13064/KSSS.2015.7.1.003
  10. Choi, Young Ho, Ban, Sung Min, Kim, Kyung-Wha and Kim, Hyung Soon, 2015, "Evaluation of Frequency Warping Based Features and Spectro-Temporal Features for Speaker Recognition", Phonetics and Speech Sciences, Vol. 7, No. 1, pp. 3-10. https://doi.org/10.13064/KSSS.2015.7.1.003
  11. Ha-Na Choi, Sung-Woo Byun and Seok-Pil Lee, 2015, "Discriminative Feature Vector Selection for Emotion Classification Based on Speech", The transactions of The Korean Institute of Electrical Engineers, Vol. 64, No. 9, pp. 1363-1368. https://doi.org/10.5370/KIEE.2015.64.9.1363
  12. Sung-Woo Byun and Seok-Pil Lee, Kunnyun Kim and Sang-Hyun Han, 2017, "Gesture recognition with wearable device based on deep learning", Broadcasting and Media Magazine, Vol. 22, No. 1, pp. 58-66.
  13. Kwang-Seung Heo, Chang-Hyun Park, Dong-Wook Lee, and Kwee-Bo Sim "speaker identification using incremental neural network and LPCC", Journal of The Korean Institute of Intelligent Systems, Volume 12 Issue 2, 2002.12. pp. 341-344. https://doi.org/10.5391/JKIIS.2002.12.4.341
  14. Hyun Woo Kim, Sung Yong Lee "The Phoneme Kernel Technique based on Support Vector Machine for Emotion Classification of Mobile Texts", Journal of KIISE: software and application, Volume 40 Issue 6 (2013.6) 350-355.