Extracting bottleneck features and word-like pairs from untranscribed speech for feature representation

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Unsupervised Speech Representation Learning Using WaveNet Autoencoders (2019, methodology): “[61] trains an autoencoder on pairs of speech segments found using an unsupervised term discovery system [79].”
Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders (2019, background): “…2015; Lyzinski et al., 2015; Zeghidour et 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…”
Multilingual bottle-neck feature learning from untranscribed speech (2017, background): “When features are derived from an internal layer of a DNN, the DNN can be an autoencoder [13], a correspondence autoencoder [14, 15] or a siamese net-”