Speaker emotion recognition: from classical classifiers to deep neural networks

Eya Mezghani; Maha Charfeddine; Henri Nicolas; Chokri Ben Amar

doi:10.1117/12.2309476

13 April 2018 Speaker emotion recognition: from classical classifiers to deep neural networks

Eya Mezghani, Maha Charfeddine, Henri Nicolas, Chokri Ben Amar

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106962M (2018) https://doi.org/10.1117/12.2309476
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

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.

Citation Download Citation

Eya Mezghani, Maha Charfeddine, Henri Nicolas, and Chokri Ben Amar "Speaker emotion recognition: from classical classifiers to deep neural networks", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106962M (13 April 2018); https://doi.org/10.1117/12.2309476

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