Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

Speaker identity is one of the important characteristics of human speech. In voice
conversion, we change the speaker identity from one to another, while kee** the linguistic …

Self-supervised learning (SSL) achieves great success in speech recognition, while limited
exploration has been attempted for other speech processing tasks. As speech signal …

Self-supervised approaches for speech representation learning are challenged by three
unique problems:(1) there are multiple sound units in each input utterance,(2) there is no …

Abstract We introduce Generative Spoken Language Modeling, the task of learning the
acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and …

Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …

This paper proposes a novel unsupervised autoregressive neural model for learning generic
speech representations. In contrast to other speech representation learning methods that …

Compared to vision and language applications, self-supervised pre-training approaches for
ASR are challenged by three unique problems:(1) There are multiple sound units in each …

We present a factorized hierarchical variational autoencoder, which learns disentangled and
interpretable representations from sequential data without supervision. Specifically, we …

In this paper, we introduce the Variational Autoencoder (VAE) to an end-to-end speech
synthesis model, to learn the latent representation of speaking styles in an unsupervised …