A Comparison of Discrete Latent Variable Models for Speech Representation Learning

Understand

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.