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arXiv · 2405.13233

MELD-ST: An Emotion-aware Speech Translation Dataset

Sirou Chen·Sakiko Yahata·Shuichiro Shimizu·Zhengdong Yang·Yihang Li·Chenhui Chu·Sadao Kurohashi
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Abstract

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.

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Sirou Chen, Sakiko Yahata, Shuichiro Shimizu, Zhengdong Yang, Yihang Li, Chenhui Chu, Sadao Kurohashi. 2024-05-21. MELD-ST: An Emotion-aware Speech Translation Dataset. https://arxiv.org/abs/2405.13233

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