Phoneme Segmentation Using Self-Supervised Speech Models

• URL: http://arxiv.org/abs/2211.01461v1
• Date: Wed, 2 Nov 2022 19:57:31 GMT
• Title: Phoneme Segmentation Using Self-Supervised Speech Models
• Authors: Luke Strgar and David Harwath
• Abstract summary: We apply transfer learning to the task of phoneme segmentation and demonstrate the utility of representations learned in self-supervised pre-training for the task. Our model extends transformer-style encoders with strategically placed convolutions that manipulate features learned in pre-training.
• Score: 13.956691231452336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We apply transfer learning to the task of phoneme segmentation and demonstrate the utility of representations learned in self-supervised pre-training for the task. Our model extends transformer-style encoders with strategically placed convolutions that manipulate features learned in pre-training. Using the TIMIT and Buckeye corpora we train and test the model in the supervised and unsupervised settings. The latter case is accomplished by furnishing a noisy label-set with the predictions of a separate model, it having been trained in an unsupervised fashion. Results indicate our model eclipses previous state-of-the-art performance in both settings and on both datasets. Finally, following observations during published code review and attempts to reproduce past segmentation results, we find a need to disambiguate the definition and implementation of widely-used evaluation metrics. We resolve this ambiguity by delineating two distinct evaluation schemes and describing their nuances.

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