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.
@article{arxiv.2405.13233,
title = {MELD-ST: An Emotion-aware Speech Translation Dataset},
author = {Sirou Chen and Sakiko Yahata and Shuichiro Shimizu and Zhengdong Yang and Yihang Li and Chenhui Chu and Sadao Kurohashi},
journal= {arXiv preprint arXiv:2405.13233},
year = {2024}
}
Comments
9 pages. Accepted to ACL 2024 Findings. Dataset: https://huggingface.co/datasets/ku-nlp/MELD-ST