The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

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

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

Learning meaningful and general representations from unannotated speech that are
applicable to a wide range of tasks remains challenging. In this paper we propose to use …

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

We propose an approach for pre-training speech representations via a masked
reconstruction loss. Our pre-trained encoder networks are bidirectional and can therefore be …

In this paper, we present a method for learning discrete linguistic units by incorporating
vector quantization layers into neural models of visually grounded speech. We show that our …

Training objectives based on predictive coding have recently been shown to be very
effective at learning meaningful representations from unlabeled speech. One example is …

We explore self-supervision as a way to learn general purpose audio representations.
Specifically, we propose two self-supervised tasks: Audio2Vec, which aims at reconstructing …

Unsupervised representation learning for speech processing has matured greatly in the last
few years. Work in computer vision and natural language processing has paved the way, but …