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

Self-supervised speech representation learning has recently been a prosperous research
topic. Many algorithms have been proposed for learning useful representations from large …

Self-supervised speech representations have been shown to be effective in a variety of
speech applications. However, existing representation learning methods generally rely on …

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 …

We present the SUPERB challenge at SLT 2022, which aims at learning self-supervised
speech representation for better performance, generalization, and efficiency. The challenge …

Recently proposed self-supervised learning approaches have been successful for pre-
training speech representation models. The utility of these learned representations has been …

Learning good representations without supervision is still an open issue in machine
learning, and is particularly challenging for speech signals, which are often characterized by …

Self-Supervised Learning (SSL) using huge unlabeled data has been successfully explored
for image and natural language processing. Recent works also investigated SSL from …

We show for the first time that learning powerful representations from speech audio alone
followed by fine-tuning on transcribed speech can outperform the best semi-supervised …

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