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Edward Lockhart

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    Parallel WaveNet: Fast High-Fidelity Speech Synthesis
    Aäron van den Oord
    Yazhe Li
    Igor Babuschkin
    Karen Simonyan
    Koray Kavukcuoglu
    George van den Driessche
    Luis Carlos Cobo Rus
    Florian Stimberg
    Norman Casagrande
    Dominik Grewe
    Seb Noury
    Sander Dieleman
    Erich Elsen
    Alexander Graves
    Helen King
    Thomas Walters
    Demis Hassabis
    NA, Google Deepmind, NA (2017)
    Preview abstract The recently-developed WaveNet architecture [27] is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system. However, because WaveNet relies on sequential generation of one audio sample at a time, it is poorly suited to today’s massively parallel computers, and therefore hard to deploy in a real-time production setting. This paper introduces Probability Density Distillation, a new method for training a parallel feed-forward network from a trained WaveNet with no significant difference in quality. The resulting system is capable of generating high-fidelity speech samples at more than 20 times faster than real-time, and is deployed online by Google Assistant, including serving multiple English and Japanese voices. View details
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