Phone Segmentation and Recognition through Phonological Activation Mapping
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Electrical Engineering and Systems Science > Audio and Speech Processing
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.
| Comments: | Code will be released after acceptance |
| Subjects: | Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD) |
| Cite as: | arXiv:2607.09020 [eess.AS] |
| (or arXiv:2607.09020v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2607.09020
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