Abstract
Performance of speech emotion recognition largely depends on the acoustic features used in a classifier. This paper studies the statistical feature selection problem in Mandarin speech emotion recognition. This study was based on a speaker dependent emotional mandarin database. Pitch, energy, duration, formant related features and some velocity information were selected as base features. Some statistics of them consisted of original feature set and full stepwise discriminant analysis (SDA) was employed to select extracted features. The results of feature selection were evaluated through a LDA based classifier. Experiment results indicate that pitch, log energy, speed and 1st formant are the most important factors and the accuracy rate increases from 63.1 % to 76.5 % after feature selection. Meanwhile, the features selected by SDA are better than the results of other feature selection methods in a LDA based classifier and SVM. The best performance is achieved when the feature number is in the range of 9 to 12.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Picard, R.W.: Affective Computing. MIT Press, Cambridge (1997)
Murray, I.R., Arnott, J.L.: Toward the Simulation of Emotion in Synthetic Speech: A Review of the Literature on Human Vocal Emotion. Journal of the Acoustical Society of America 93(2), 1097–1108 (1933)
Dellaert, F., Polzin, T., Waibel, A.: Recognizing Emotion in Speech. In: Proceedings of International Conference on Spoken Language Processing, pp. 1970–1973 (1996)
Lee, C.M., Narayanan, S., Pieraccini, R.: Recognition of Negative Emotions from the Speech Signal. In: Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 240–243 (2001)
Kwon, O.W., Chan, K., Hao, J., Lee, T.W.: Emotion Recognition by Speech Signals. In: Proceedings of EUROSPEECH, pp. 125–128 (2003)
Wang, Z.P., Zhao, L., Zou, C.R.: Emotion Recognition of Speech using Fuzzy Entropy Effectiveness Analysis. Journal of circuits and systems 8(3), 109–112 (2003)
James, M.: Classification Algorithms. John Wiley & Sons, London (1985)
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., et al.: Emotion Recognition in Human-computer Interaction. IEEE Signal Processing Magazine 18(1), 32–80 (2001)
Cai, L.L., Jiang, C.H., Wang, Z.P.: A Method Combining the Global and Time Series Structure Features for Emotion Recognition in Speech. In: Proceedings of International Conference on Neural Networks and Signal Processing, pp. 904–907 (2003)
Boersma, P., Weenink, D.: Praat Speech Processing Software. Institute of Phonetics Sciences of the University of Amsterdam, http://www.praat.org
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xie, B., Chen, L., Chen, GC., Chen, C. (2005). Statistical Feature Selection for Mandarin Speech Emotion Recognition. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_62
Download citation
DOI: https://doi.org/10.1007/11538059_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28226-6
Online ISBN: 978-3-540-31902-3
eBook Packages: Computer ScienceComputer Science (R0)