et al., 2020) combines normalizing ﬂows with Tacotron 2
for an end-to-end structure but remains autoregressive. The
audio quality of all the aforementioned end-to-end TTS
models is less than that of two-stage models.
Unlike the aforementioned end-to-end models, by utiliz-
ing a conditional VAE, our model 1) learns to synthesize
raw waveforms directly from text without requiring addi-
tional input conditions, 2) uses a dynamic programming
method, MAS, to search the optimal alignment rather than
to calculate loss, 3) generates samples in parallel, and 4)
outperforms the best publicly available two-stage models.
ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech