Publication

vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations

International Conference on Learning Representations (ICLR)


Abstract

We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task. The algorithm uses either a Gumbel-Softmax or online k-means clustering to quantize the dense representations. Discretization enables the direct application of algorithms from the NLP community which require discrete inputs. Experiments show that BERT pre-training achieves a new state of the art on TIMIT phoneme classification and WSJ speech recognition.

Related Publications

All Publications

AISTATS - April 13, 2021

Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors

Nikhil Mehta, Kevin J Liang, Vinay K Verma, Lawrence Carin

NeurIPS - December 6, 2020

Improved Sample Complexity for Incremental Autonomous Exploration in MDPs

Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

NeurIPS - December 7, 2020

Labelling unlabelled videos from scratch with multi-modal self-supervision

Yuki M. Asano, Mandela Patrick, Christian Rupprecht, Andrea Vedaldi

NeurIPS - December 7, 2020

Adversarial Example Games

Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy