Abstract
In the article we evaluate the importance of different HMM states in an HMM-based feature extraction method used to model paralinguistic information. Specifically, we evaluate the distribution of the paralinguistic information across different states of the HMM in two different classification tasks: emotion recognition and alcoholization detection. In the task of recognizing emotions we found that the majority of emotion-related information is incorporated in the first and third state of a 3-state HMM. Surprisingly, in the alcoholization detection task we observed a somewhat equal distribution of task-specific information across all three states, resulting in constantly producing better results if more states are utilized.
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Gajšek, R., Dobrišek, S., Mihelič, F. (2012). Analysis and Assessment of State Relevance in HMM-Based Feature Extraction Method. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2012. Lecture Notes in Computer Science(), vol 7499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32790-2_68
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DOI: https://doi.org/10.1007/978-3-642-32790-2_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32789-6
Online ISBN: 978-3-642-32790-2
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