{"ID":2881250,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14920","arxiv_id":"2508.14920","title":"Human Feedback Driven Dynamic Speech Emotion Recognition","abstract":"This work proposes to explore a new area of dynamic speech emotion recognition. Unlike traditional methods, we assume that each audio track is associated with a sequence of emotions active at different moments in time. The study particularly focuses on the animation of emotional 3D avatars. We propose a multi-stage method that includes the training of a classical speech emotion recognition model, synthetic generation of emotional sequences, and further model improvement based on human feedback. Additionally, we introduce a novel approach to modeling emotional mixtures based on the Dirichlet distribution. The models are evaluated based on ground-truth emotions extracted from a dataset of 3D facial animations. We compare our models against the sliding window approach. Our experimental results show the effectiveness of Dirichlet-based approach in modeling emotional mixtures. Incorporating human feedback further improves the model quality while providing a simplified annotation procedure.","short_abstract":"This work proposes to explore a new area of dynamic speech emotion recognition. Unlike traditional methods, we assume that each audio track is associated with a sequence of emotions active at different moments in time. The study particularly focuses on the animation of emotional 3D avatars. We propose a multi-stage met...","url_abs":"https://arxiv.org/abs/2508.14920","url_pdf":"https://arxiv.org/pdf/2508.14920v1","authors":"[\"Ilya Fedorov\",\"Dmitry Korobchenko\"]","published":"2025-08-18T17:25:27Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.HC\",\"cs.LG\",\"eess.AS\"]","methods":"[]","has_code":false}