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.