ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech

Understand

In this work, we propose a new solution for parallel wave generation by WaveNet.

• In contrast to parallel WaveNet (van den Oord et al., 2018), we distill a Gaussian inverse autoregressive flow from the autoregressive WaveNet by minimizing a regularized KL divergence between their highly-peaked output distributions.
• Our method computes the KL divergence in closed-form, which simplifies the training algorithm and provides very efficient distillation.
• In addition, we introduce the first text-to-wave neural architecture for speech synthesis, which is fully convolutional and enables fast end-to-end training from scratch.