AQAScore: 通过音频问答评估文本到音频生成中的语义对齐
AQAScore: Evaluating Semantic Alignment in Text-to-Audio Generation via Audio Question Answering
AI总结：
AQAScore通过音频问答评估文本到音频生成的语义对齐，利用大型语言模型的推理能力，有效捕捉语义不一致性。
摘要：
尽管文本到音频生成在真实感和多样性方面取得了显著进展，但评估指标的发展并未同步。广泛采用的方法通常基于嵌入相似性，如CLAPScore，虽然能有效衡量一般相关性，但在细粒度语义对齐和组合推理方面存在局限。为了解决这一问题，我们引入了AQAScore，一种不依赖特定模型的评估框架，利用具有音频意识的大型语言模型（ALLMs）的推理能力。AQAScore将评估重新表述为一个概率语义验证任务；而不是依赖开放式的文本生成，它通过计算针对特定语义查询的“是”答案的精确对数概率来估计对齐情况。我们评估了AQAScore在多个基准上的表现，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，AQAScore在与人类判断的相关性方面始终优于基于相似性度量和生成提示基线的指标，显示出其在捕捉细微语义不一致性和随基础ALLMs能力提升而扩展的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
