Abstract
The contribution describes experiments with recognition of emotions in German speech signal based on the same principle as recognition of speakers. The most robust algorithm for speaker recognition is based on Gaussian Mixture Models (GMM). We examine three parameter sets: the first contains suprasegmental features, in the second are segmental features and the last is a combination of the two previous parameter sets. Further we want to explore the dependency of the classification accuracy on the number of GMM model components. The aim of this contribution is a recommendation for the number of GMM components and the optimal selection of speech parameters for emotion recognition in German speech.
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© 2009 Springer-Verlag Berlin Heidelberg
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Vondra, M., Vích, R. (2009). Recognition of Emotions in German Speech Using Gaussian Mixture Models. In: Esposito, A., Hussain, A., Marinaro, M., Martone, R. (eds) Multimodal Signals: Cognitive and Algorithmic Issues. Lecture Notes in Computer Science(), vol 5398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00525-1_26
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DOI: https://doi.org/10.1007/978-3-642-00525-1_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00524-4
Online ISBN: 978-3-642-00525-1
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