Phone Segmentation and Recognition through Phonological Activation Mapping
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| Authors | Shikhar Bharadwaj et al. |
| Year | 2026 |
| HF Upvotes | 4 |
| arXiv | 2607.09020 |
| Download | |
| HF Page | View on Hugging Face |
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.
Engineering Breakdown
The Problem
Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately.
The Approach
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.
Key Results
Across a diverse range of datasets, our approach attains strong segmentation and recognition performance.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Segmentation
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