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This research demonstrates that phonetic structure is latent within self-supervised speech model representations, enabling a unified approach to phone segmentation and recognition via Phonological Activation Mapping (SPAM). By utilizing lightweight, gradient-descent-free prediction heads, the method achieves strong performance with minimal data requirements while generalizing to unseen phones. This work advances the efficiency and capability of speech processing architectures by leveraging existing model internals rather than training separate pipelines.
Phone Segmentation and Recognition through Phonological Activation Mapping
arXiv:2607.09020v2 Announce Type: replace-cross 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), so 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 the recognition head generalizes to phones unseen during training. Across a diverse range of datasets, our approach attains strong segmentation performance.
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
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), so 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 the recognition head generalizes to phones unseen during training. Across a diverse range of datasets, our approach attains strong segmentation performance.
Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD)
arXiv:2607.09020 [eess.AS]
(or arXiv:2607.09020v2 [eess.AS] for this version)
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