Modeling and Transforming Speech Using Variational Autoencoders

By Merlijn Blaauw, Jordi Bonada. 2016

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Citations in the corpus

Variational Autoencoders Pursue PCA Directions (by Accident) (2018, background): “As for representation learning, there is a variety of applications, ranging over image interpolation [19], one-shot generalization [35], language models [43], speech transformation [3], and more.”
Learning Latent Representations for Speech Generation and Transformation (2017, methodology): “A fully-connected VAE was used in [14] to learn a frame-level latent representation, and evaluated using a Gaussian diffusion process to generate and concatenate multiple samples that varied smoothly in time.”
Voice Conversion from Unaligned Corpora Using Variational Autoencoding Wasserstein Generative Adversarial Networks (2017, background): “Recent works have proven the viability of speech modeling with VAEs [3, 4].”
Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder (2018, background): “Several recent studies have proposed using VAEs for modeling speech [20, 21, 14, 22, 23].”
Dynamical Variational Autoencoders: A Comprehensive Review (2020, methodology): “, (Blaauw and Bonada, 2016; Hsu et al., 2017a; Esling et al., 2018; Roche et al., 2019), or as a prior distribution of x in more complex Bayesian models for, e.”
StarGAN-VC: non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks (2018, background): “Recently, some attempts have been made to develop non-parallel methods [17–29].”
Deep Encoder-Decoder Models for Unsupervised Learning of Controllable Speech Synthesis (2018, background): “For example, [42], [49]–[51] all consider applying VAEs to each frame in an acoustic analysis of speech, with the intention of learning to encode something similar to phonetic identity in the absence of transcription.”