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

April 16, 2020

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.

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AUTHORS

Written by

Alexei Baevski

Michael Auli

Steffen Schneider

Publisher

ICLR

Research Topics

Natural Language Processing (NLP)

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