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• W2979476256 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." @default.
• W2979476256 created "2019-10-18" @default.
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• W2979476256 date "2019-10-12" @default.
• W2979476256 modified "2023-10-01" @default.
• W2979476256 title "vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations" @default.
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