similar architecture. The difference is that the posterior encoder transforms a Gaussian noise sequence into two random
variables ν and u to express the approximate posterior distribution qφ(u, ν|d, ctext), and the normalizing ﬂow module
transforms d − u and ν into a Gaussian noise sequence to express the log-likelihood of the augmented and dequantized data
log pθ(d − u, ν|ctext) as described in Section 2.2.2.
All input conditions are processed through condition encoders, each consisting of two 1x1 convolutional layers and a
DDSConv residual block. The posterior encoder and normalizing ﬂow module have four coupling layers of neural spline
ﬂows. Each coupling layer ﬁrst processes input and input conditions through a DDSConv block and produces 29-channel
parameters that are used to construct 10 rational-quadratic functions. We set the hidden dimension of all coupling layers and
ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech