通过结合自举数据选择的语音到语音翻译改进低资源语言中的语音情感识别
Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection
AI总结：
针对非英、中语言情感标注稀缺问题，提出用表达性 S2ST 与自举数据选择生成目标语言标注数据，提升多语言 SER 的跨模型与跨语言泛化性能。
摘要：
语音情感识别（SER）是开发能够实现自然人机交互的通用 AI 智能体的关键组成部分。然而，由于英语和中文以外语言的标注数据稀缺，构建鲁棒的多语言 SER 系统仍然具有挑战性。本文提出一种方法，通过利用高资源语言的数据来提升低 SER 资源语言的性能。具体而言，我们采用富有表现力的语音到语音翻译（S2ST），并结合一种新颖的自举数据选择流程，以生成目标语言的标注数据。大量实验表明，我们的方法在不同上游模型和语言之间既有效又具有泛化性。结果表明，该方法有助于开发更具可扩展性和鲁棒性的多语言 SER 系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
