Abstract
The current study focuses on speech emotion recognition based on a hierarchical classification scheme. The study aims at overcoming the problem of low accuracy in the case of a large number of emotions that are considered in a specific task. In the proposed method, the emotions are classified based on the valence-arousal 2-dimensional map, and models are trained for each group. In a second pass, with-in group recognition is performed for the group selected in the previous stage.
Dr. Panikos Heracleous is currently with Artificial Intelligence Research Center (AIRC), AIST, Japan.
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Heracleous, P., Takai, K., Yasuda, K., Yoneyama, A. (2021). A Hierarchical Classification Scheme for Efficient Speech Emotion Recognition. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Late Breaking Posters. HCII 2021. Communications in Computer and Information Science, vol 1499. Springer, Cham. https://doi.org/10.1007/978-3-030-90179-0_12
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