An RNN-based algorithm to detect prosodic phrase for Chinese TTS

By Zhiwei Ying, Xiao-dong Shi. 2001

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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”
A Unified Sequence-to-Sequence Front-End Model for Mandarin Text-to-Speech Synthesis (2019, methodology): “The prosody structure in Mandarin TTS determines the naturalness to a great extent, which has been proved in previous researches [5, 6].”
DurIAN: Duration Informed Attention Network for Speech Synthesis (2020, background): “Therefore, it is still more desirable to have a separate model for learning word and prosody boundaries from larger dataset [13, 14, 15] in tasks such as Mandarin speech synthesis.”