Abstract
The paper analyzes the prosody features, which includes the intonation, speaking rate, intensity, based on classified emotional speech. As an important feature of voice quality, voice source are also deduced for analysis. With the analysis results above, the paper creates both a CART model and a weight decay neural network model to find acoustic importance towards the emotional speech classification and to disclose whether there is an underlying consistency between acoustic features and speech emotion. The result shows the proposed method can obtain the importance of each acoustic feature through its weight for emotional speech classification and further improve the emotional speech classification.
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© 2005 Springer-Verlag Berlin Heidelberg
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Tao, J., Kang, Y. (2005). Features Importance Analysis for Emotional Speech Classification. In: Tao, J., Tan, T., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2005. Lecture Notes in Computer Science, vol 3784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_58
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DOI: https://doi.org/10.1007/11573548_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29621-8
Online ISBN: 978-3-540-32273-3
eBook Packages: Computer ScienceComputer Science (R0)