Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-level Embedding Features

By Zexin Cai, Yaogen Yang, Chuxiong Zhang, Xiaoyi Qin, Ming Li. 2019

28 citations recorded by the source corpus.

This paper describes a conditional neural network architecture for Mandarin Chinese polyphone disambiguation. The system is composed of a bidirectional recurrent neural network component acting as a sentence encoder to accumulate the context correlations, followed by a prediction network that maps the polyphonic character embeddings along with the conditions to corresponding pronunciations. We obtain the word-level condition from a pre-trained word-to-vector lookup table. One goal of polyphone disambiguation is to address the homograph problem existing in the front-end processing of Mandarin Chinese text-to-speech system. Our system achieves an accuracy of 94.69\% on a publicly available polyphonic character dataset. To further validate our choices on the conditional feature, we investigate polyphone disambiguation systems with multi-level conditions respectively. The experimental results show that both the sentence-level and the word-level conditional embedding features are able to attain good performance for Mandarin Chinese polyphone disambiguation.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Sheng Zhao
5. Frank K. Soong
6. Yi Ren
7. Zhou Zhao
8. Yang Zhang

Citations in the corpus

• 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].”
• g2pM: A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese Based on a New Open Benchmark Dataset (2020, methodology): “Recently [1, 4] use bidirectional Long Short-Term Memory (LSTM) [11] to extract diverse features on the character, word, and sentence level.”
• Knowledge Distillation from Bert in Pre-Training and Fine-Tuning for Polyphone Disambiguation (2019, background): “In recent years, neural networks such as LSTM have improved the accuracy of polyphone disambiguation significantly [10, 18].”
• 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).”