Deep learning methods for unsupervised acoustic modeling — Leap submission to ZeroSpeech challenge 2017

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Unsupervised Speech Representation Learning Using WaveNet Autoencoders (2019, background): “[60] extended this scheme with features discovered by an autoencoder trained on”
The zero resource speech challenge 2017 (2017, background): “Ansari et al. [25] combine two sets of features, also trained on all ﬁve languages.”
Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders (2019, background): “…al., 2016; Heck et al., 2016; Srivastava and Shrivastava, 2016; Kamper et al., 2017b; Chen et al., 2017; Yuan et al., 2017; Heck et al., 2017; Shibata et al., 2017; Ansari et al., 2017a,b) perform unsupervised ABX discrimination and/or spoken term discovery on the basis of unlabeled speech alone.”