@inproceedings{selvakumar-etal-2025-audio,
    title = "Do Audio-Language Models Understand Linguistic Variations?",
    author = "Selvakumar, Ramaneswaran  and
      Kumar, Sonal  and
      Giri, Hemant Kumar  and
      Anand, Nishit  and
      Seth, Ashish  and
      Ghosh, Sreyan  and
      Manocha, Dinesh",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-short.76/",
    pages = "899--913",
    ISBN = "979-8-89176-190-2",
    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. We make our code publicly available"
}