Towards the Next Frontier in Speech Representation Learning Using Disentanglement
This work addresses the need for better speech representations that capture both fine-grained and coarse factors like speaker characteristics, offering incremental improvements over existing self-supervised methods.
The authors tackled the problem of speech representation learning by proposing a framework that disentangles frame-level and utterance-level encoders, achieving state-of-the-art results on various tasks with specific improvements in semantic and non-semantic domains.
The popular frameworks for self-supervised learning of speech representations have largely focused on frame-level masked prediction of speech regions. While this has shown promising downstream task performance for speech recognition and related tasks, this has largely ignored factors of speech that are encoded at coarser level, like characteristics of the speaker or channel that remain consistent through-out a speech utterance. In this work, we propose a framework for Learning Disentangled Self Supervised (termed as Learn2Diss) representations of speech, which consists of frame-level and an utterance-level encoder modules. The two encoders are initially learned independently, where the frame-level model is largely inspired by existing self supervision techniques, thereby learning pseudo-phonemic representations, while the utterance-level encoder is inspired by constrastive learning of pooled embeddings, thereby learning pseudo-speaker representations. The joint learning of these two modules consists of disentangling the two encoders using a mutual information based criterion. With several downstream evaluation experiments, we show that the proposed Learn2Diss achieves state-of-the-art results on a variety of tasks, with the frame-level encoder representations improving semantic tasks, while the utterance-level representations improve non-semantic tasks.