Speech emotion recognition (SER) is a pivotal technology for human-computer interaction systems. However, 80.77% of SER papers yield results that cannot be reproduced. We develop EMO-SUPERB, short for EMOtion Speech Universal PERformance Benchmark, which aims to enhance open-source initiatives for SER. EMO-SUPERB includes a user-friendly codebase to leverage 15 state-of-the-art speech self-supervised learning models (SSLMs) for exhaustive evaluation across six open-source SER datasets. EMO-SUPERB streamlines result sharing via an online leaderboard, fostering collaboration within a community-driven benchmark and thereby enhancing the development of SER. On average, 2.58% of annotations are annotated using natural language. SER relies on classification models and is unable to process natural languages, leading to the discarding of these valuable annotations. We prompt ChatGPT to mimic annotators, comprehend natural language annotations, and subsequently re-label the data. By utilizing labels generated by ChatGPT, we consistently achieve an average relative gain of 3.08% across all settings.

翻译：语音情感识别（SER）是实现人机交互系统的关键技术。然而，80.77%的SER论文结果无法复现。我们开发了EMO-SUPERB（EMOtion Speech Universal PERformance Benchmark），旨在促进SER领域的开源研究。EMO-SUPERB提供一个用户友好的代码库，能够利用15个最先进的语音自监督学习模型（SSLMs），在六个开源SER数据集上进行全面评估。该系统通过在线排行榜简化结果共享流程，推动社区驱动的基准测试协作，从而加速SER技术的发展。平均而言，数据集中2.58%的标注采用自然语言形式。由于SER依赖分类模型且无法处理自然语言，这些有价值的标注常被丢弃。我们引导ChatGPT模拟标注者理解自然语言标注，并重新标记数据。实验表明，使用ChatGPT生成的标签后，所有设置下的平均相对性能提升达3.08%。