CAF-Score: Calibrating CLAP with LALMs for Reference-free Audio Captioning Evaluation
Abstract
While Large Audio-Language Models (LALMs) have advanced audio captioning, robust evaluation remains difficult. Reference-based metrics are expensive and often fail to assess acoustic fidelity, while Contrastive Language-Audio Pretraining (CLAP)-based approaches frequently overlook syntactic errors and fine-grained details. We propose CAF-Score, a reference-free metric that calibrates CLAP's coarse-grained semantic alignment with the fine-grained comprehension and syntactic awareness of LALMs. By combining contrastive audio-text embeddings with LALM reasoning, CAF-Score effectively detects syntactic inconsistencies and subtle hallucinations. Experiments on the BRACE benchmark demonstrate that our approach achieves the highest correlation with human judgments, even outperforming reference-based baselines in challenging scenarios. These results highlight the efficacy of CAF-Score for reference-free audio captioning evaluation. Code and results are available at https://github.com/inseong00/CAF-Score.
Cite
@article{arxiv.2603.19615,
title = {CAF-Score: Calibrating CLAP with LALMs for Reference-free Audio Captioning Evaluation},
author = {Insung Lee and Taeyoung Jeong and Haejun Yoo and Du-Seong Chang and Myoung-Wan Koo},
journal= {arXiv preprint arXiv:2603.19615},
year = {2026}
}
Comments
A condensed version of this work has been submitted to Interspeech 2026. Section 10 is an extended analysis added in this version