Enhancing emotion recognition from speech through feature selection

This source preferred by Theodoros Kostoulas

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Authors: Kostoulas, T., Ganchev, T., Lazaridis, A. and Fakotakis, N.

Editors: Sojka, P., Horák, A., Kopecek, I. and Pala, K.

https://doi.org/10.1007/978-3-642-15760-8

Volume: 6231

Pages: 338-344

Publisher: Springer

ISBN: 978-3-642-15759-2

This data was imported from Scopus:

Authors: Kostoulas, T., Ganchev, T., Lazaridis, A. and Fakotakis, N.

Volume: 6231 LNAI

Pages: 338-344

ISBN: 9783642157592

DOI: 10.1007/978-3-642-15760-8_43

In the present work we aim at performance optimization of a speaker-independent emotion recognition system through speech feature selection process. Specifically, relying on the speech feature set defined in the Interspeech 2009 Emotion Challenge, we studied the relative importance of the individual speech parameters, and based on their ranking, a subset of speech parameters that offered advantageous performance was selected. The affect-emotion recognizer utilized here relies on a GMM-UBM-based classifier. In all experiments, we followed the experimental setup defined by the Interspeech 2009 Emotion Challenge, utilizing the FAU Aibo Emotion Corpus of spontaneous, emotionally coloured speech. The experimental results indicate that the correct choice of the speech parameters can lead to better performance than the baseline one. © 2010 Springer-Verlag Berlin Heidelberg.

The data on this page was last updated at 04:57 on May 24, 2019.