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Found 2 papers, 1 papers with code
A Comparison of Discrete Latent Variable Models for Speech Representation Learning
Neural latent variable models enable the discovery of interesting structure in speech audio data.
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler.
Ranked #1 on
Speech Recognition
on TIMIT
(using extra training data)
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