A probabilistic interpretation for artificial neural network-based voice conversion

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Neural Voice Cloning with a Few Samples (2018, methodology): “There are also approaches to model spectral conversion using neural networks [Desai et al., 2010, Chen et al., 2014, Hwang et al., 2015].”
Voice conversion from non-parallel corpora using variational auto-encoder (2016, methodology): “A wide variety of techniques have been applied to spectral conversion, including Gaussian mixture models (GMM) [1]– [3], frequency warping [4], [5], deep neural networks (DNN) [6]–[8], and exemplar-based approaches [9], [10].”
Voice Conversion from Unaligned Corpora Using Variational Autoencoding Wasserstein Generative Adversarial Networks (2017, background): “However, similar inconsistency is common in the VC literature because it is highly likely that those evaluations are inconsistent with human auditory systems [10, 11, 12].”
Locally Linear Embedding for Exemplar-Based Spectral Conversion (2016, methodology): “Numerous VC approaches have been proposed, such as Gaussian mixture model (GMM)-based [1-4], frequency warping-based [5, 6], neural networks (NN)-based [7-10], and exem-plar-based [11-13] approaches.”