Human-CLAP: Human-perception-based contrastive language-audio pretraining

Taisei Takano; Yuki Okamoto; Yusuke Kanamori; Yuki Saito; Ryotaro Nagase; Hiroshi Saruwatari

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arXiv · 2506.23553

Human-CLAP: Human-perception-based contrastive language-audio pretraining

Taisei Takano·Yuki Okamoto·Yusuke Kanamori·Yuki Saito·Ryotaro Nagase·Hiroshi Saruwatari

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Abstract

Contrastive language-audio pretraining (CLAP) is widely used for audio generation and recognition tasks. For example, CLAPScore, which utilizes the similarity of CLAP embeddings, has been a major metric for the evaluation of the relevance between audio and text in text-to-audio. However, the relationship between CLAPScore and human subjective evaluation scores is still unclarified. We show that CLAPScore has a low correlation with human subjective evaluation scores. Additionally, we propose a human-perception-based CLAP called Human-CLAP by training a contrastive language-audio model using the subjective evaluation score. In our experiments, the results indicate that our Human-CLAP improved the Spearman's rank correlation coefficient (SRCC) between the CLAPScore and the subjective evaluation scores by more than 0.25 compared with the conventional CLAP.

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BibTeX RIS

Taisei Takano, Yuki Okamoto, Yusuke Kanamori, Yuki Saito, Ryotaro Nagase, Hiroshi Saruwatari. 2025-06-30. Human-CLAP: Human-perception-based contrastive language-audio pretraining. https://arxiv.org/abs/2506.23553

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