MELD-ST: An Emotion-aware Speech Translation Dataset

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


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.
Anthology ID:
2024.findings-acl.601
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10118–10126
Language:
URL:
https://aclanthology.org/2024.findings-acl.601
DOI:
10.18653/v1/2024.findings-acl.601
Bibkey:
Cite (ACL):
Sirou Chen, Sakiko Yahata, Shuichiro Shimizu, Zhengdong Yang, Yihang Li, Chenhui Chu, and Sadao Kurohashi. 2024. MELD-ST: An Emotion-aware Speech Translation Dataset. In Findings of the Association for Computational Linguistics: ACL 2024, pages 10118–10126, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
MELD-ST: An Emotion-aware Speech Translation Dataset (Chen et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-acl.601.pdf