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
- 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)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-acl.601.pdf