| title | Phone Segmentation and Recognition through Phonological Activation Mapping |
|---|---|
| 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 |
| date | 2026-07-10 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.09020 |
| affiliation | ChangeLing Lab |
| upvotes | 4 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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. |
cite
@misc{bharadwaj2026phone,
title={Phone Segmentation and Recognition through Phonological Activation Mapping},
author={Shikhar Bharadwaj and Kwanghee Choi and Stephen McIntosh and Chin-Jou Li and Eunjung Yeo and Daisuke Saito and Nobuaki Minematsu and Shinji Watanabe and Jian Zhu and David Harwath and David R. Mortensen},
year={2026},
eprint={2607.09020},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.09020}
}