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arXiv · 2607.09020

Phone Segmentation and Recognition through Phonological Activation Mapping

Shikhar Bharadwaj·Kwanghee Choi·Stephen McIntosh·Chin-Jou Li·Eunjung Yeo·Daisuke Saito·Nobuaki Minematsu·Shinji Watanabe·Jian Zhu·David Harwath·David R. Mortensen
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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.

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Shikhar Bharadwaj, Kwanghee Choi, Stephen McIntosh, Chin-Jou Li, Eunjung Yeo, Daisuke Saito, Nobuaki Minematsu, Shinji Watanabe, Jian Zhu, David Harwath, David R. Mortensen. 2026-07-10. Phone Segmentation and Recognition through Phonological Activation Mapping. https://arxiv.org/abs/2607.09020

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