Text normalization with convolutional neural networks

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A Survey on Neural Speech Synthesis (2021, methodology): “Task Research Work Text Normalization Rule-based [311], Neural-based [310, 223, 406, 430], Hybrid [432] Word Segmentation [394, 444, 261] POS Tagging [292, 323, 221, 444, 135] Prosody Prediction [50, 405, 312, 186, 137, 322, 277, 62, 440, 210, 212, 3] Grapheme to Phoneme N-gram [41, 24], Neural-base”
Neural Text Normalization with Subword Units (2019, methodology): “Recent data-driven approaches examine window-based sequence-to-sequence (seq2seq) models and convolutional neural networks (CNN) to normalize a central piece of text with the help of context (Sproat and Jaitly, 2016; Yolchuyeva et al., 2018).”
Neural Models of Text Normalization for Speech Applications (2019, methodology): “In general, Pramanik and Hussain’s system does better than any of the systems reported by Yolchuyeva et al., but for most categories, our own system does better than either. Our system is tied with the DNC system for PUNCT, CARDINAL, MONEY, and TIME. The DNC system gets the highest accuracies for DA”