Abstract
Although text-to-audio generation has made remarkable progress in realism and diversity, the development of evaluation metrics has not kept pace. Widely-adopted approaches, typically based on embedding similarity like CLAPScore, effectively measure general relevance but remain limited in fine-grained semantic alignment and compositional reasoning. To address this, we introduce AQAScore, a backbone-agnostic evaluation framework that leverages the reasoning capabilities of audio-aware large language models (ALLMs). AQAScore reformulates assessment as a probabilistic semantic verification task; rather than relying on open-ended text generation, it estimates alignment by computing the exact log-probability of a "Yes" answer to targeted semantic queries. We evaluate AQAScore across multiple benchmarks, including human-rated relevance, pairwise comparison, and compositional reasoning tasks. Experimental results show that AQAScore consistently achieves higher correlation with human judgments than similarity-based metrics and generative prompting baselines, showing its effectiveness in capturing subtle semantic inconsistencies and scaling with the capability of underlying ALLMs.
Abstract (translated)
尽管从文本到语音的生成技术在真实感和多样性方面取得了显著进展，但评估指标的发展却未能跟上步伐。目前广泛采用的方法通常基于嵌入相似度（如CLAPScore），能够有效测量一般相关性，但在细粒度语义对齐和组合推理方面仍存在局限性。为了解决这个问题，我们引入了AQAScore，这是一个与骨干网络无关的评估框架，利用音频感知大型语言模型（ALLMs）的能力进行推理。 AQAScore将评估重构为一个概率性的语义验证任务；它通过计算特定语义查询中“是”的回答的确切对数概率来估算对齐情况，而不是依赖于开放式文本生成。我们使用多个基准测试对AQAScore进行了评估，包括人工评分的相关性、成对比较和组合推理任务。 实验结果表明，与基于相似性的度量标准和生成提示基线相比，AQAScore在各种人类判断上保持了更高的相关性，这证明它在捕捉细微的语义不一致以及随着底层ALLMs能力的增长而扩展方面具有有效性。
URL
https://arxiv.org/abs/2601.14728
