  We propose a self-supervised representation learning model for the task of
unsupervised phoneme boundary detection. The model is a convolutional neural
network that operates directly on the raw waveform. It is optimized to identify
spectral changes in the signal using the Noise-Contrastive Estimation
principle. At test time, a peak detection algorithm is applied over the model
outputs to produce the final boundaries. As such, the proposed model is trained
in a fully unsupervised manner with no manual annotations in the form of target
boundaries nor phonetic transcriptions. We compare the proposed approach to
several unsupervised baselines using both TIMIT and Buckeye corpora. Results
suggest that our approach surpasses the baseline models and reaches
state-of-the-art performance on both data sets. Furthermore, we experimented
with expanding the training set with additional examples from the Librispeech
corpus. We evaluated the resulting model on distributions and languages that
were not seen during the training phase (English, Hebrew and German) and showed
that utilizing additional untranscribed data is beneficial for model
performance.
