  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
