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EMO-SUPERB: An In-depth Look at Speech Emotion Recognition

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arxiv 2402.13018 v4 pith:THACARPV submitted 2024-02-20 eess.AS cs.SD

Haibin Wu , Huang-Cheng Chou , Kai-Wei Chang , Lucas Goncalves , Jiawei Du , Jyh-Shing Roger Jang , Chi-Chun Lee , Hung-Yi Lee This is my paper · ORCID
classification eess.AScs.SD
keywords emo-superbspeechannotationsemotionnaturalacrossaveragebenchmark
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Speaker-Aware Temporal Aggregation Strategies on Segment Representations for Depression Detection in Dyadic Interaction: A Benchmark Study

    eess.AS 2026-07 conditional novelty 6.0 of 10

    Across six pooling heads and six frozen SSL backbones on English and Mandarin depression speech, a third of configurations collapse to single-class prediction, so backbone- and seed-robustness should be first-class ev...

  2. MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge

    cs.AI 2025-05 conditional novelty 6.0 of 10

    GPT-4o text-only prompts can re-annotate a Friends-based emotion dataset, and models trained on those labels outperform models trained on the original human labels in cross-corpus speech emotion recognition.

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