SEA-SpeechBench - Emotion Recognition (SEA Prompt): leaderboard
Metric: Judge-based accuracy (%; closed nine-class emotion label from speech, free-form answers mapped to labels and judged by Gemma-3-27B-Instruct, per-dataset scores averaged; short clips of at most 30 s from curated public Southeast Asian speech corpora, up to 1,000 sampled items per dataset; instruction prompt in the native Southeast Asian language). Source: arxiv.org. Saturation forecast: Around 2030. 15 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | GPT-4o Audio | 19.5 |
| 2 | Qwen2-Audio-7B-Instruct | 19.36 |
| 3 | Gemini 2.5 Flash | 16.79 |
| 4 | Gemma 3n E4B (IT) | 13.93 |
| 5 | gemma-3n-E2B-it | 13.17 |
| 6 | Qwen3 Omni 30B A3B Instruct | 11.31 |
| 7 | Qwen2.5-Omni-7B | 10.45 |
| 8 | Phi-4 Multimodal Instruct | 9.2 |
| 9 | SeaLLMs-Audio-7B | 9.17 |
| 10 | Voxtral-Mini-3B-2507 | 5.35 |
Interactive version: theaggregate.ai/benchmark?slug=sea-speechbench-emotion-recognition-sea-prompt · How It Works · Data refreshed daily, snapshot 2026-09-26.