Mandarin Prosodic Phrase Prediction based on Syntactic Trees

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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”
Unified Mandarin TTS Front-end Based on Distilled BERT Model (2020, methodology): “, syntactic trees based rules for PSP [3], dictionary matching et al based polyphone disambiguity [4]), or statistical learning models (like CRF [5] and LSTM/Attention based NN models [6, 7] for PSP, [8, 9] for Mandarin G2P).”
Implementing Prosodic Phrasing in Chinese End-to-end Speech Synthesis (2019, background): “Besides this word grouping, there are also higher level groupings [19][20].”
Review of end-to-end speech synthesis technology based on deep learning (2021, methodology): “These components are usually modeled by traditional statistical methods, such as syntactic trees [264] and CRF [167] based methods for PSP tasks and dictionary matching based methods [77] for pronunciation prediction tasks.”