Enhancing Multilingual Recognition of Emotion in Speech by Language Identification

Hesam Sagha, Pavel Matějka, Maryna Gavryukova, Filip Povolny, Erik Marchi, Björn Schuller


We investigate, for the first time, if applying model selection based on automatic language identification (LID) can improve multilingual recognition of emotion in speech. Six emotional speech corpora from three language families (Germanic, Romance, Sino-Tibetan) are evaluated. The emotions are represented by the quadrants in the arousal/valence plane, i. e., positive/ negative arousal/valence. Four selection approaches for choosing an optimal training set depending on the current language are compared: within the same language family, across language family, use of all available corpora, and selection based on the automatic LID. We found that, on average, the proposed LID approach for selecting training corpora is superior to using all the available corpora when the spoken language is not known.


DOI: 10.21437/Interspeech.2016-333

Cite as

Sagha, H., Matějka, P., Gavryukova, M., Povolny, F., Marchi, E., Schuller, B. (2016) Enhancing Multilingual Recognition of Emotion in Speech by Language Identification. Proc. Interspeech 2016, 2949-2953.

Bibtex
@inproceedings{Sagha+2016,
author={Hesam Sagha and Pavel Matějka and Maryna Gavryukova and Filip Povolny and Erik Marchi and Björn Schuller},
title={Enhancing Multilingual Recognition of Emotion in Speech by Language Identification},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-333},
url={http://dx.doi.org/10.21437/Interspeech.2016-333},
pages={2949--2953}
}