and designing a specialized spectrogram loss to relax length-
mismatch between target and generated speech. However,
despite potentially improving performance by utilizing the
learned representations, their synthesis quality lags behind
two-stage systems.
In this work, we present a parallel end-to-end TTS method
that generates more natural sounding audio than cur-
rent two-stage models. Using a variational autoencoder
(VAE) (Kingma & Welling, 2014), we connect two modules
of TTS systems through latent variables to enable efﬁcient
end-to-end learning. To improve the expressive power of
our method so that high-quality speech waveforms can be
synthesized, we apply normalizing ﬂows to our conditional
prior distribution and adversarial training on the waveform
domain. In addition to generating ﬁne-grained audio, it is
important for TTS systems to express the one-to-many rela-
tionship in which text input can be spoken in multiple ways
ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech