An efficient way to learn rules for grapheme-to-phoneme conversion in Chinese

By Zi-rong Zhang, Min Chu, Eric Chang. 2002

26 citations recorded by the source corpus.

How researchers see this paper

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

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Frank K. Soong
5. Sheng Zhao
6. Yi Ren
7. Zexin Cai
8. Jun-Wei Gan

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, methodology): “Further, WaveRNN [441] is developed for efficient audio synthesis, using a recurrent neural network and leveraging several designs including dual softmax layer, weight pruning, and subscaling techniques to reduce the computation.”
• Grapheme-to-phoneme conversion for Chinese text-to-speech (2004, methodology): “To implement the three modules in Chinese G2P, many approaches have been proposed [3,4,6,7,11,12].”
• A bi-directional LSTM approach for polyphone disambiguation in Mandarin Chinese (2016, background): “So the difficulty in Chinese G2P conversion is polyphone disambiguation, which aims to pick out one correct pronunciation from several candidates for a polyphonic character [8].”
• Knowledge Distillation from Bert in Pre-Training and Fine-Tuning for Polyphone Disambiguation (2019, background): “Therefore, the G2P conversion task in these languages is to select the correct pronunciation of a polyphone from the candidates [1, 2].”
• Inequality Maximum Entropy Classifier with Character Features for Polyphone Disambiguation in Mandarin TTS Systems (2007, background): “631% of the whole corpus and their distributions are similarly to [15].”
• Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-level Embedding Features (2019, methodology): “Rule-based algorithms [12, 13, 14] and data-driven methods [15, 16, 17] are two frequently used approaches for polyphone disambiguation.”