vq-wav2vec: Self-Supervised Learning of Discrete Speech RepresentationsDownload PDF

25 Sept 2019, 19:28 (modified: 10 Feb 2022, 11:41)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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Code: [![Papers with Code](/images/pwc_icon.svg) 3 community implementations](https://paperswithcode.com/paper/?openreview=rylwJxrYDS)
Data: [LibriSpeech](https://paperswithcode.com/dataset/librispeech), [TIMIT](https://paperswithcode.com/dataset/timit)
TL;DR: Learn how to quantize speech signal and apply algorithms requiring discrete inputs to audio data such as BERT.
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.
Keywords: speech recognition, speech representation learning
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