Phone Segmentation and Recognition through Phonological Activation Mapping
Authors: name: Shikhar Bharadwaj, name: Kwanghee Choi, name: Stephen McIntosh, name: Chin-Jou Li, name: Eunjung Yeo, name: Daisuke Saito, name: Nobuaki Minematsu, name: Shinji Watanabe, name: Jian Zhu, name: David Harwath, name: David R. Mortensen
Organization: ChangeLing Lab

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.