  While discrete latent variable models have had great success in
self-supervised learning, most models assume that frames are independent. Due
to the segmental nature of phonemes in speech perception, modeling dependencies
among latent variables at the frame level can potentially improve the learned
representations on phonetic-related tasks. In this work, we assume Markovian
dependencies among latent variables, and propose to learn speech
representations with neural hidden Markov models. Our general framework allows
us to compare to self-supervised models that assume independence, while keeping
the number of parameters fixed. The added dependencies improve the
accessibility of phonetic information, phonetic segmentation, and the cluster
purity of phones, showcasing the benefit of the assumed dependencies.
