@inproceedings{parra-2025-interpretable,
    title = "Interpretable Sparse Features for Probing Self-Supervised Speech Models",
    author = "Parra, I{\~n}igo",
    editor = "T.y.s.s, Santosh  and
      Shimizu, Shuichiro  and
      Gong, Yifan",
    booktitle = "The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-srw.1/",
    pages = "1--9",
    ISBN = "979-8-89176-304-3",
    abstract = "Self-supervised speech models have demonstrated the ability to learn rich acoustic representations. However, interpreting which specific phonological or acoustic features these models leverage within their highly polysemantic activations remains challenging. In this paper, we propose a straightforward and unsupervised probing method for model interpretability. We extract the activations from the final MLP layer of a pretrained HuBERT model and train a sparse autoencoder (SAE) using dictionary learning techniques to generate an over-complete set of latent representations. Analyzing these latent codes, we observe that a small subset of high-variance units consistently aligns with phonetic events, suggesting their potential utility as interpretable acoustic detectors. Our proposed method does not require labeled data beyond raw audio, providing a lightweight and accessible tool to gain insights into the internal workings of self-supervised speech models."
}