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Speaker emotion recognition: from classical classifiers to deep neural networks
Author(s): Eya Mezghani; Maha Charfeddine; Henri Nicolas; Chokri Ben Amar
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Paper Abstract

Speaker emotion recognition is considered among the most challenging tasks in recent years. In fact, automatic systems for security, medicine or education can be improved when considering the speech affective state. In this paper, a twofold approach for speech emotion classification is proposed. At the first side, a relevant set of features is adopted, and then at the second one, numerous supervised training techniques, involving classic methods as well as deep learning, are experimented. Experimental results indicate that deep architecture can improve classification performance on two affective databases, the Berlin Dataset of Emotional Speech and the SAVEE Dataset Surrey Audio-Visual Expressed Emotion.

Paper Details

Date Published: 13 April 2018
PDF: 7 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106962M (13 April 2018); doi: 10.1117/12.2309476
Show Author Affiliations
Eya Mezghani, Univ. de Sfax (Tunisia)
Maha Charfeddine, Univ. de Sfax (Tunisia)
Henri Nicolas, Univ. Bordeaux (France)
Chokri Ben Amar, Univ. de Sfax (Tunisia)


Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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