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A Comparison of Discrete Latent Variable Models for Speech Representation Learning

2020-10-24 01:22:14
Henry Zhou, Alexei Baevski, Michael Auli
arXiv_AI
         
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

Abstract (translated)

URL

https://arxiv.org/abs/2010.14230

PDF

https://arxiv.org/pdf/2010.14230.pdf

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