用大模型做语音质量评估，能解释还能跨语言通用。
SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation
- 让大模型像人一样分析语音质量并给出理由。
- 在32,207段多语言语音上测试，表现优于传统方法。
- 适合需要可解释评估的语音生成、反诈检测场景。
生成式语音技术发展迅速，但合成语音的感知质量评估仍是核心挑战。现有方法通常依赖单一评分或二分类判断，缺乏可解释性且难以跨任务和语言泛化。本文提出SpeechLLM-as-Judges新范式，使大语言模型（LLMs）能够进行结构化、带解释的语音质量评估。为此，我们构建了SpeechEval数据集，包含32,207个多语言语音片段及128,754条标注，覆盖质量评估、成对比较、改进建议和深度伪造检测四类任务。基于此，我们训练了具备语音质量感知能力的SQ-LLM模型，采用思维链推理与奖励优化策略提升性能。实验表明，SQ-LLM在多任务、多语言场景下表现优异，展现出该范式在推动语音质量评估方面的重要潜力。相关代码、模型与数据已开源。
原文摘要 · Abstract (English)
Generative speech technologies are progressing rapidly, but evaluating the perceptual quality of synthetic speech remains a core challenge. Existing methods typically rely on scalar scores or binary decisions, which lack interpretability and generalization across tasks and languages. We present SpeechLLM-as-Judges, a new paradigm for enabling large language models (LLMs) to conduct structured and explanation-based speech quality evaluation. To support this direction, we introduce SpeechEval, a large-scale dataset containing 32,207 multilingual speech clips and 128,754 annotations spanning four tasks: quality assessment, pairwise comparison, improvement suggestion, and deepfake detection. Based on this resource, we develop SQ-LLM, a speech-quality-aware LLM trained with chain-of-thought reasoning and reward optimization to improve capability. Experimental results show that SQ-LLM delivers strong performance across tasks and languages, revealing the potential of this paradigm for advancing speech quality evaluation. The relevant code, models, and data are publicly available at https://github.com/NKU-HLT/SpeechLLM-as-Judges.
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