WaveNet is a deep convolutional artificial neural network. It is also an autoregressive and probabilistic generative model; it is therefore by nature perfectly suited to solving various complex problems in speech processing. It already achieves state-of-the-art performance in text-to-speech synthesis. It also constitutes a radically new and remarkably efficient tool to perform voice transformation, speech enhancement, and speech compression. This paper presents a comprehensive review of the literature on WaveNet since its introduction in 2016. It identifies and discusses references related to its theoretical foundation, its application scope, and the possible optimization of its subjective quality and computational efficiency.

Authors: Boilard, Jonathan; Gournay, Philippe; Lefebvre, Roch
Affiliation: Universite de Sherbrooke, Sherbrooke, Quebec, Canada
AES Convention: 146 (March 2019) Paper Number: 10171
Publication Date: March 10, 2019
Subject: Machine Learning: Part 2

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