Speech Emotion Diarization: Which Emotion Appears When?
Yingzhi Wang, Mirco Ravanelli, A. El Yacoubi
Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
Speech Emotion Recognition (SER) typically relies on utterance-level solutions. However, emotions conveyed through speech should be considered as discrete speech events with definite temporal boundaries, rather than attributes of the entire utterance. To reflect the fine-grained nature of speech emotions and to unify various fine-grained methods under a single objective, we propose a new task: Speech Emotion Diarizat ...
ion (SED). Just as Speaker Diarization answers the question of “Who speaks when?”, Speech Emotion Diarization answers the question of “Which emotion appears when?”. To facilitate the evaluation of the performance and establish a common benchmark, we introduce the Zaion Emotion Dataset (ZED), an openly accessible speech emotion dataset that includes non-acted emotions recorded in real-life conditions, along with manually annotated boundaries of emotion segments within the utterance. We provide competitive baselines and open-source the code and the pre-trained models.
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Speech Emotion Recognition (SER) typically relies on utterance-level solutions.
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Research context
17
Citations
39
References
Tasks
Utterance, Speaker diarisation, Computer science, Task (project management), Emotion recognition, Benchmark (surveying), Emotion classification, Emotion detection
Methods
Transformer
Domains
Speech recognition, Natural language processing, Artificial intelligence
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