Contrastive Prediction Strategies for Unsupervised Segmentation and Categorization of Phonemes and Words

We identify a performance trade-off between the tasks of phoneme categorization and phoneme and word segmentation in several self-supervised learning algorithms based on Contrastive Predictive Coding (CPC). Our experiments suggest that context building networks, albeit necessary for high performance on categorization tasks, harm segmentation performance by causing a temporal shift on the learned representations. Aiming to tackle this trade-off, we take inspiration from the leading approaches on segmentation and propose multi-level Aligned CPC (mACPC). It builds on Aligned CPC (ACPC), a variant of CPC which exhibits the best performance on categorization tasks, and incorporates multi-level modeling and optimization for detection of spectral changes. Our methods improve in all tested categorization metrics and achieve state-of-the-art performance in word segmentation.