UniWav: Towards Unified Pre-training for Speech Representation Learning and Generation
Alexander H. Liu Sang-gil Lee Chao-Han Huck Yang Yuan Gong Yu-Chiang Frank Wang James R. Glass Rafael Valle Bryan Catanzaro
Pre-training and representation learning have been playing an increasingly important role in modern speech processing.  Nevertheless, different applications have been relying on different foundation models, since predominant pre-training techniques are either designed for discriminative tasks or generative tasks. In this work, we make the first attempt at building a unified pre-training framework for both types of tasks in speech. We show that with the appropriate design choices for pre-training, one can jointly learn a representation encoder and generative audio decoder that can be applied to both types of tasks. We propose UniWav, an encoder-decoder framework designed to unify pre-training representation learning and generative tasks. On speech recognition, text-to-speech, and speech tokenization, UniWav achieves comparable performance to different existing foundation models, each trained on a specific task. Our findings suggest that a single general-purpose foundation model for speech can be built to replace different foundation models, reducing the overhead and cost of pre-training.
speech foundation model, generative pre-training, self-supervised learning, speech generation, speech tokenization
First unified pre-training method for speech representation learning and generation