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

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arxiv 1910.05453 v3 pith:KZT3ZSOY submitted 2019-10-12 cs.CL cs.LG

Alexei Baevski , Steffen Schneider , Michael Auli This is my paper · ORCID
classification cs.CLcs.LG
keywords discreterepresentationsself-supervisedspeechvq-wav2vecachievesalgorithmalgorithms
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

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