Binary Representation Learning for Discriminative Acoustic Unit Discovery

Abstract

Acoustic Unit Discovery (AUD) aims to obtain phoneme-like units that preserve linguistically significant information while removing paralinguistic details. Although Contrastive Predictive Coding (CPC) has emerged as a leading self-supervised representation learning method for this task, CPC-based methods still suffer from the limitation that the learned representations are susceptible to paralinguistic information and less discriminative. Inspired by the theory of distinctive features, we propose a new approach that builds a binary discriminative representation space and employs binary contrastive learning based on CPC to tackle the aforementioned issues. Experimental results show that our method achieves better results in AUD task and produces discriminative binary representations.