Speech Emotion Recognition (SER) is a crucial component in developing general-purpose AI agents capable of natural human-computer interaction. However, building robust multilingual SER systems remains challenging due to the scarcity of labeled data in languages other than English and Chinese. In this paper, we propose an approach to enhance SER performance in low SER resource languages by leveraging data from high-resource languages. Specifically, we employ expressive Speech-to-Speech translation (S2ST) combined with a novel bootstrapping data selection pipeline to generate labeled data in the target language. Extensive experiments demonstrate that our method is both effective and generalizable across different upstream models and languages. Our results suggest that this approach can facilitate the development of more scalable and robust multilingual SER systems.

本研究解决了多语言语音情感识别系统在欠资源语言中因标记数据稀缺而面临的挑战。研究提出了一种结合表达性语音到语音翻译和新颖自助数据选择流程的方法，以生成目标语言的标记数据。实验表明，该方法有效且具有较好的通用性，能够推动多国语言情感识别系统的发展。

通过语音到语音翻译和自助数据选择改善欠资源语言中的语音情感识别