Bayesian Subspace HMM for the Zerospeech 2020 Challenge

19 May 2020Bolaji YusufLucas Ondel

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... (read more)

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