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This paper introduces Parallel Tacotron 2, a non-autoregressive neuraltext-to-speech model with a fully differentiable duration model which does notrequire supervised duration signals. The duration model is based on a novelattention mechanism and an iterative reconstruction loss based on Soft DynamicTime Warping, this model can learn token-frame alignments as well as tokendurations automatically. Experimental results show that Parallel Tacotron 2outperforms baselines in subjective naturalness in several diverse multispeaker evaluations. Its duration control capability is also demonstrated.

Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling