  Explicit duration modeling is a key to achieving robust and efficient
alignment in text-to-speech synthesis (TTS). We propose a new TTS framework
using explicit duration modeling that incorporates duration as a discrete
latent variable to TTS and enables joint optimization of whole modules from
scratch. We formulate our method based on conditional VQ-VAE to handle discrete
duration in a variational autoencoder and provide a theoretical explanation to
justify our method. In our framework, a connectionist temporal classification
(CTC) -based force aligner acts as the approximate posterior, and
text-to-duration works as the prior in the variational autoencoder. We
evaluated our proposed method with a listening test and compared it with other
TTS methods based on soft-attention or explicit duration modeling. The results
showed that our systems rated between soft-attention-based methods
(Transformer-TTS, Tacotron2) and explicit duration modeling-based methods
(Fastspeech).
