Inequality Maximum Entropy Classifier with Character Features for Polyphone Disambiguation in Mandarin TTS Systems

By Xinnian Mao, Yuan Dong, Jinyu Han, Dezhi Huang, Haila Wang. 2007

17 citations recorded by the source corpus.

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A Survey on Neural Speech Synthesis (2021, methodology): “Thus, G2P conversion in this kind of languages is mainly responsible for polyphone disambiguation, which decides the appropriate pronunciation based on the current word context [441, 392, 224, 295, 321, 29, 257].”
A Unified Sequence-to-Sequence Front-End Model for Mandarin Text-to-Speech Synthesis (2019, methodology): “The most popular method for PD is to apply the ME model for each polyphone [10].”
g2pM: A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese Based on a New Open Benchmark Dataset (2020, methodology): “They can be categorized into the traditional rulebased approach [5, 6, 7] and the data driven approach [1, 2, 3, 4, 10].”
Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-level Embedding Features (2019, methodology): “[16] F. Z. Liu and Y. Zhou, “Polyphone Disambiguation Based on Maximum Entropy Model in Mandarin Grapheme-to-Phoneme Conversion,” Key Engineering Materials, vol. 480-481, pp. 1043– 1048, 2011.”
Knowledge Distillation from Bert in Pre-Training and Fine-Tuning for Polyphone Disambiguation (2019, methodology): “Therefore, the G2P conversion task in these languages is to select the correct pronunciation of a polyphone from the candidates [1, 2].”