  Recent neural text-to-speech (TTS) models with fine-grained latent features
enable precise control of the prosody of synthesized speech. Such models
typically incorporate a fine-grained variational autoencoder (VAE) structure,
extracting latent features at each input token (e.g., phonemes). However,
generating samples with the standard VAE prior often results in unnatural and
discontinuous speech, with dramatic prosodic variation between tokens. This
paper proposes a sequential prior in a discrete latent space which can generate
more naturally sounding samples. This is accomplished by discretizing the
latent features using vector quantization (VQ), and separately training an
autoregressive (AR) prior model over the result. We evaluate the approach using
listening tests, objective metrics of automatic speech recognition (ASR)
performance, and measurements of prosody attributes. Experimental results show
that the proposed model significantly improves the naturalness in random sample
generation. Furthermore, initial experiments demonstrate that randomly sampling
from the proposed model can be used as data augmentation to improve the ASR
performance.
