VoxEmo: Benchmarking Speech Emotion Recognition with Speech LLMs
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 di...
| Authors | Hezhao Zhang, Huang-Cheng Chou, Shrikanth Narayanan et al. |
|---|---|
| Primary category | cs.SD |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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