Sirou Chen


2024

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MELD-ST: An Emotion-aware Speech Translation Dataset
Sirou Chen | Sakiko Yahata | Shuichiro Shimizu | Zhengdong Yang | Yihang Li | Chenhui Chu | Sadao Kurohashi
Findings of the Association for Computational Linguistics ACL 2024

Emotion plays a crucial role in human conversation. This paper underscores the significance of considering emotion in speech translation. We present the MELD-ST dataset for the emotion-aware speech translation task, comprising English-to-Japanese and English-to-German language pairs. Each language pair includes about 10,000 utterances annotated with emotion labels from the MELD dataset. Baseline experiments using the SeamlessM4T model on the dataset indicate that fine-tuning with emotion labels can enhance translation performance in some settings, highlighting the need for further research in emotion-aware speech translation systems.