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MELD-ST: An Emotion-aware Speech Translation Dataset

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arxiv 2405.13233 v1 pith:2FDVD4BQ submitted 2024-05-21 cs.CL

Sirou Chen , Sakiko Yahata , Shuichiro Shimizu , Zhengdong Yang , Yihang Li , Chenhui Chu , Sadao Kurohashi This is my paper · ORCID
classification cs.CL
keywords translationdatasetemotionspeechemotion-awarelabelslanguagemeld-st
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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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