Large Vocabulary Continuous Speech Recognition : a ReviewSteve

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HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units (2021, methodology): “In the extreme case when α = 0, the loss is computed over the unmasked timesteps, which is similar to acoustic modeling in hybrid speech recognition systems [35]–[38].”
Unsupervised Speech Recognition (2021, methodology): “Such models are often classified into two categories: hybrid (Juang et al., 1986; Young, 1996; Povey, 2005; Hinton et al., 2012; Abdel-Hamid et al., 2012; Bourlard and Morgan, 2012) or end-to-end (Graves et al., 2006; Graves, 2012; Chorowski et al., 2015; Rao et al., 2017).”
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups (2012, unknown): “…had the same size, but even with this constraint it was impossible to train all possible combinations of number of hidden layers [1] [2] [3] [4] [5] [6] [7] [8], number of units per layer [512] [1] [024] [2] [048] [3] [072], and number of frames of acoustic data in the input layer [7] [11] [15]…”
Deep Neural Networks for Acoustic Modeling in Speech Recognition (2012, background): “…had the same size, but even with this constraint it was impossible to train all possible combinations of number of hidden layers [1] [2] [3] [4] [5] [6] [7] [8], number of units per layer [512] [1] [024] [2] [048] [3] [072] and number of frames of acoustic data in the input layer [7] [11] [15]…”