  In this paper we describe our submission to the Zerospeech 2020 challenge,
where the participants are required to discover latent representations from
unannotated speech, and to use those representations to perform speech
synthesis, with synthesis quality used as a proxy metric for the unit quality.
In our system, we use the Bayesian Subspace Hidden Markov Model (SHMM) for unit
discovery. The SHMM models each unit as an HMM whose parameters are constrained
to lie in a low dimensional subspace of the total parameter space which is
trained to model phonetic variability. Our system compares favorably with the
baseline on the human-evaluated character error rate while maintaining
significantly lower unit bitrate.
