  This paper introduces Parallel Tacotron 2, a non-autoregressive neural
text-to-speech model with a fully differentiable duration model which does not
require supervised duration signals. The duration model is based on a novel
attention mechanism and an iterative reconstruction loss based on Soft Dynamic
Time Warping, this model can learn token-frame alignments as well as token
durations automatically. Experimental results show that Parallel Tacotron 2
outperforms baselines in subjective naturalness in several diverse multi
speaker evaluations. Its duration control capability is also demonstrated.
