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vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations
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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 8 Pith papers
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PairAlign: A Framework for Sequence Tokenization via Self-Alignment with Applications to Audio Tokenization
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