Abstract
The following paper introduces a set of novel descriptors of emotional speech, which allows for a significant increase in emotion classification performance. The proposed characteristics - statistical properties of Poincare Maps, derived for voiced-speech segments of utterances - are used in recognition in combinations with a variety of both commonly used and some other, original descriptors of emotional speech. The introduced features proved to provide useful information into a classification process. Emotion recognition is performed using binary decision trees, which perform extraction of different emotions at consecutive decision levels. Classification rates for the considered six-category problem, which involved anger, boredom, joy, fear, neutral and sadness, are at the level up to 79% for both speaker-dependent and speaker-independent cases.
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
Kappas, A., Hess, U., Scherer, K.R.: Voice and emotion: Fundamentals of Nonverbal Behavior. In: Rim, B., Feldman, R.S. (eds.), pp. 200–238. Cambridge University Press, Cambridge (1991)
Scherer, K.R.: Vocal correlates of emotion: Emotion and Social Behavior. In: Wagner, H., Manstead, A. (eds.) Handbook of Psychophysiology, pp. 165–197. Wiley, London (1989)
Scherer, K.R.: Vocal affect expression: A review and a model for future research. Psychol. Bull. 99(2), 143–165 (1986)
Batliner, A., Huber, R., Spilker, J.: The recognition of Emotion. In: Int. Conf. on Spoken Language Processing, pp. 122–130 (2000)
Scherer, K.R.: Vocal communication of emotion: A review of research paradigms. Speech Communication 40, 227–256 (2003)
Cowie, R., et al.: Emotion recognition in human-computer interaction. IEEE Signal Processing magazine 18(1), 32–80 (2001)
Douglas-Cowie, E., Cowie, R., Schroder, M.: Speech and emotion. Speech Communication 40, 1–257 (2003)
Scherer, K.R.: Expression of emotion in voice and music. J. Voice 9(3), 235–248 (1995)
Zhou, G., Hansen, J.H.L., Kaiser, J.F.: Nonlinear feature based classification of speech under stress. IEEE Transactions on Speech and Audio Processing 9(3), 201–216 (2001)
Nwe, T.L., Foo, S.W., De Silva, L.C.: Speech emotion recognition using hidden Markov models. Speech Communication 41, 603–623 (2003)
Kwon, O.K., Chan, K., Hao, J., Lee, T.W.: Emotion recognition by speech signals. In: Int. Conf. EUROSPEECH 2003, Geneva, Switzerland, pp. 125–128 (2003)
Dellaert, F., Polzin, T., Waibel, A.: Recognizing emotion in speech. In: International Conference on Spoken Language Processing (ICSLP), pp. 1970–1973 (1996)
Nogueiras, A., Moreno, A., Bonafonte, A., Marino, J.B.: Speech emotion recognition using Hidden Markov Models. In: Proceedings of Eurospeech, Aalborg, Denmark, pp. 2679–2682 (2001)
Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W., Weiss, B.: A Database of German Emotional Speech. In: Proc. of Int. Conf. Interspeech 2005, Lisbon, pp. 1517–1520 (2005)
Skijarov, O., Bortnik, B.: Chaos and speech rhythm. In: International Joint Conference on Neural Network, vol. 4, pp. 2070–2075 (2005)
Bronakowski, lot, K., Cichosz, J., Kim, J.: Application of Poincare Map-Based Description of Vowel Pronunciation Variability for Emotion Assessment in Speech Signal. In: Int. Symp. on Information Technology Convergence (ISITC 2007), Korea, pp. 175–178 (2007)
Boersma, P., Weenink, D.: PRAAT, a system for doing phonetics by computer. Glot International 5(9/10), 341–345 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ślot, K., Cichosz, J., Bronakowski, L. (2008). Emotion Recognition with Poincare Mapping of Voiced-Speech Segments of Utterances. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_84
Download citation
DOI: https://doi.org/10.1007/978-3-540-69731-2_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69572-1
Online ISBN: 978-3-540-69731-2
eBook Packages: Computer ScienceComputer Science (R0)