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）是人机交互系统中的关键技术。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，情感语音通用性能基准），旨在推动SER领域的开源倡议。EMO-SUPERB包含一个用户友好的代码库，可利用15种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。通过在线排行榜简化结果共享，EMO-SUPERB促进了社区驱动基准内的协作，从而推动SER的发展。平均有2.58%的标注采用自然语言进行标注。SER依赖分类模型，无法处理自然语言，导致这些有价值标注被丢弃。我们提示ChatGPT模仿标注者，理解自然语言标注，并随后对数据重新标注。通过使用ChatGPT生成的标签，我们在所有设置中平均获得了3.08%的相对增益。