{"ID":6500358,"CreatedAt":"2026-07-13T03:11:43.660365884Z","UpdatedAt":"2026-07-14T02:54:43.516908796Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.09020","arxiv_id":"2607.09020","title":"Phone Segmentation and Recognition through Phonological Activation Mapping","abstract":"Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic structure is already latent in the representations of self-supervised speech models (S3Ms), and one only needs to steer them to solve both tasks. We leverage S3M-based Phonological Activation Mapping (SPAM), which maps each S3M representation frame to a vector of phonological feature activations, such as voicing and nasality. On top of SPAM, we introduce two simple but effective lightweight, gradient-descent-free prediction heads: a recognition head and a segmentation head. Our method requires less than a minute of phonetic transcriptions, and generalizes to unseen phones during training. Across a diverse range of datasets, our approach attains strong segmentation and recognition performance.","short_abstract":"Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic structure is already latent in the representations of self-supervised speech models (S3Ms), and one only needs to steer them to solve both tasks. We leverage S3M-based Phonologi...","url_abs":"https://arxiv.org/abs/2607.09020","url_pdf":"https://arxiv.org/pdf/2607.09020v1","authors":"[\"Shikhar Bharadwaj\",\"Kwanghee Choi\",\"Stephen McIntosh\",\"Chin-Jou Li\",\"Eunjung Yeo\",\"Daisuke Saito\",\"Nobuaki Minematsu\",\"Shinji Watanabe\",\"Jian Zhu\",\"David Harwath\",\"David R. Mortensen\"]","published":"2026-07-10T01:05:30Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"cs.AI\",\"cs.CL\",\"cs.LG\",\"cs.SD\"]","methods":"[]","has_code":false}