Do Audio-Language Models Understand Linguistic Variations?

• URL: http://arxiv.org/abs/2410.16505v1
• Date: Mon, 21 Oct 2024 20:55:33 GMT
• Title: Do Audio-Language Models Understand Linguistic Variations?
• Authors: Ramaneswaran Selvakumar, Sonal Kumar, Hemant Kumar Giri, Nishit Anand, Ashish Seth, Sreyan Ghosh, Dinesh Manocha,
• Abstract summary: Open-vocabulary audio language models (ALMs) represent a promising new paradigm for audio-text retrieval using natural language queries. We propose RobustCLAP, a novel and compute-efficient technique to learn audio-language representations to linguistic variations.
• Score: 42.17718387132912
• License:
• Abstract: Open-vocabulary audio language models (ALMs), like Contrastive Language Audio Pretraining (CLAP), represent a promising new paradigm for audio-text retrieval using natural language queries. In this paper, for the first time, we perform controlled experiments on various benchmarks to show that existing ALMs struggle to generalize to linguistic variations in textual queries. To address this issue, we propose RobustCLAP, a novel and compute-efficient technique to learn audio-language representations agnostic to linguistic variations. Specifically, we reformulate the contrastive loss used in CLAP architectures by introducing a multi-view contrastive learning objective, where paraphrases are treated as different views of the same audio scene and use this for training. Our proposed approach improves the text-to-audio retrieval performance of CLAP by 0.8%-13% across benchmarks and enhances robustness to linguistic variation.

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