MELD-ST: An Emotion-aware Speech Translation Dataset

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 Englishto-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.