Semi-Supervised DNN Training with Word Selection for ASR

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End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures (2019, neutral): “, 2019a) for both low-resource (Veselỳ et al., 2017; Cui et al., 2017) and large-scale (Parthasarathi & Strom, 2019) setups.”
Unsupervised Speech Recognition (2021, methodology): “, 2020a), or with separate objectives and weights (Veselỳ et al., 2017; Manohar et al., 2018; Hsu et al., 2020).”
Semi-Supervised Speech Recognition via Local Prior Matching (2020, background): “, 2017; Parthasarathi & Strom, 2019) and weak distillation (also known as selftraining or pseudo-labeling) (Veselỳ et al., 2017; Manohar et al., 2018; Li et al., 2019; Kahn et al., 2019a) have also achieved great success in semi-supervised learning for ASR.”
Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text Data (2018, background): “Although substantial efforts have been reported on semi-supervised ASR [1, 2, 3, 4, 5, 6], in most cases a large amount of speech data including a good portion annotated were still needed.”