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arXiv · 2010.14230

A Comparison of Discrete Latent Variable Models for Speech Representation Learning

Henry Zhou·Alexei Baevski·Michael Auli
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Abstract

Neural latent variable models enable the discovery of interesting structure in speech audio data. This paper presents a comparison of two different approaches which are broadly based on predicting future time-steps or auto-encoding the input signal. Our study compares the representations learned by vq-vae and vq-wav2vec in terms of sub-word unit discovery and phoneme recognition performance. Results show that future time-step prediction with vq-wav2vec achieves better performance. The best system achieves an error rate of 13.22 on the ZeroSpeech 2019 ABX phoneme discrimination challenge

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BibTeXRIS

Henry Zhou, Alexei Baevski, Michael Auli. 2020-10-24. A Comparison of Discrete Latent Variable Models for Speech Representation Learning. https://arxiv.org/abs/2010.14230

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