Computer Science > Sound
arXiv:2603.08936 (cs)
[Submitted on 9 Mar 2026]
Title:VoxEmo: Benchmarking Speech Emotion Recognition with Speech LLMs
View a PDF of the paper titled VoxEmo: Benchmarking Speech Emotion Recognition with Speech LLMs, by Hezhao Zhang and 3 other authors
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Abstract:Speech Large Language Models (LLMs) show great promise for speech emotion recognition (SER) via generative interfaces. However, shifting from closed-set classification to open text generation introduces zero-shot stochasticity, making evaluation highly sensitive to prompts. Additionally, conventional speech LLMs benchmarks overlook the inherent ambiguity of human emotion. Hence, we present VoxEmo, a comprehensive SER benchmark encompassing 35 emotion corpora across 15 languages for Speech LLMs. VoxEmo provides a standardized toolkit featuring varying prompt complexities, from direct classification to paralinguistic reasoning. To reflect real-world perception/application, we introduce a distribution-aware soft-label protocol and a prompt-ensemble strategy that emulates annotator disagreement. Experiments reveal that while zero-shot speech LLMs trail supervised baselines in hard-label accuracy, they uniquely align with human subjective distributions.
| Comments: | |
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2603.08936 [cs.SD] |
| (or arXiv:2603.08936v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08936
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View a PDF of the paper titled VoxEmo: Benchmarking Speech Emotion Recognition with Speech LLMs, by Hezhao Zhang and 3 other authors
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