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Recent work has explored sequence-to-sequence latent variable models forexpressive speech synthesis (supporting control and transfer of prosody andstyle), but has not presented a coherent framework for understanding thetrade-offs between the competing methods. In this paper, we propose embeddingcapacity (the amount of information the embedding contains about the data) as aunified method of analyzing the behavior of latent variable models of speech,comparing existing heuristic (non-variational) methods to variational methodsthat are able to explicitly constrain capacity using an upper bound onrepresentational mutual information. In our proposed model (Capacitron), weshow that by adding conditional dependencies to the variational posterior suchthat it matches the form of the true posterior, the same model can be used forhigh-precision prosody transfer, text-agnostic style transfer, and generationof natural-sounding prior samples. For multi-speaker models, Capacitron is ableto preserve target speaker identity during inter-speaker prosody transfer andwhen drawing samples from the latent prior. Lastly, we introduce a method fordecomposing embedding capacity hierarchically across two sets of latents,allowing a portion of the latent variability to be specified and the remainingvariability sampled from a learned prior. Audio examples are available on theweb.

Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis