  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.
