  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.
