G2P 中文转拼音 多音字消歧 论文总结
论文链接均在作者Github下，大家多多专注呀！
1. 字形转音素
1.1 基于规则
(1998) Issues in building general letter to sound rules
1.2 基于联合序列模型
(2002) Pronunciation of proper names with a joint n-gram model for bi-directional grapheme-to-phoneme conversion
(2008) Joint-sequence models for grapheme-to-phoneme conversion
(ICASSP 2017) Bayesian_joint-sequence_models_for_grapheme-to-phoneme_conversion
1.3 基于编码-解码器结构
(Interspeech 2015) Sequence-to-sequence neural net models for grapheme-to-phoneme conversion
(Interspeech 2017) Multitask sequence-to-sequence models for grapheme-to-phoneme conversion
(ICASSP 2018) Convolutional sequence to sequence model with non-sequential greedy decoding for grapheme to phoneme conversion
(2019) Grapheme-to-phoneme conversion with convolutional neural networks
(ICASSP 2022) Neural_Grapheme-To-Phoneme_Conversion_with_Pre-Trained_Grapheme_Models
(ICASSP 2021) Joint_Alignment_Learning-Attention_Based_Model_for_Grapheme-to-Phoneme_Conversion
1.4 基于长短时记忆循环神经网络
(ICASSP 2015) GRAPHEME-TO-PHONEME CONVERSION USING LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORKS
1.5 基于多语言编码
(2016) Grapheme-to-phoneme models for any language
(2017) Massively multilingual neural grapheme-to-phoneme conversion
(2018) Epitran: Precision g2p for many languages
1.6 基于小样本的循环神经网络模型
(ICASSP 2017) Low-resource_grapheme-to-phoneme_conversion_using_recurrent_neural_networks
2. 中文转拼音
2.1 基于规则
(1998) Chinese sentence tokenization using Viterbi decoder
(2004) Grapheme-to-phoneme conversion for Chinese text-to-speech
(2005) Grapheme-to-phoneme_conversion_in_Chinese_TTS_system
(2006) 汉语字音转换中的多层面多音字读音消歧
(2008) 中文TTS系统中多音字的一种解决方案
(2008)Disambiguating_effectively_Chinese_polyphonic_ambiguity_based_on_unify_approach
(2010) Disambiguation for polyphones of Chinese based on two-pass unified approach
2.2 基于机器学习
2.2.1 基于决策树分类
(1996) Comparison of Two Tree-Structured Approaches for Grapheme-to-Phoneme Conversion
(1996) The broad study of homograph disambiguity for mandarin speech synthesis
(2002) An efficient way to learn rules for grapheme-to-phoneme conversion in Chinese
(2010) Polyphonic word disambiguation with machine learning approaches
2.2.2 基于最大熵
(2007) 基于最大熵模型的多音字消歧
(2007) Inequality maximum entropy classifier with character features for polyphone disambiguation in mandarin tts systems
(2011) Polyphone disambiguation based on maximum entropy model in mandarin grapheme-to-phoneme conversion
2.2.4 基于TBL算法
(2005) Grapheme-to-phoneme conversion based on TBL algorithm in Mandarin TTS systems
(ICASSP2008) Tree-guided transformation-based homograph disambiguation in Mandarin TTS system
2.3 基于深度学习
2.3.1 基于LSTM
(ICASSP 2016) A Bi-directional LSTM Approach for Polyphone Disambiguation in Mandarin Chinese
(2020) 基于深度学习的中文至拼音首字母自动转化方法
(2021)A Method of Polyphone Disambiguation Based on Semantic Extension
2.3.2 基于神经网络
(2019) Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-level Embedding Features
(Interspeech 2020) A Mask-based Model for Mandarin Chinese Polyphone Disambiguation
2.3.3 基于Encoder_Decoder结构
(Interspeech 2019) Disambiguation of Chinese Polyphones in an End-to-End Framework with Semantic Features Extracted by Pre-Trained BERT
(2020) Multilingual grapheme-to-phoneme conversion with byte representation
(2020) Neural Machine Translation for Multilingual Grapheme-to-Phoneme Conversion
2.3.4 基于预训练模型
(Interspeech 2019) Pre-Trained Text Representations for Improving Front-End Text Processing in Mandarin Text-to-Speech Synthesis
(ASRU 2019) Knowledge_Distillation_from_Bert_in_Pre-Training_and_Fine-Tuning_for_Polyphone_Disambiguation
(ICASSP 2021) A Universal Bert-Based Front-End Model for Mandarin Text-To-Speech Synthesis
(ICASSP 2022) Data_Augmentation_for_Long-Tailed_and_Imbalanced_Polyphone_Disambiguation_in_Mandarin
2.4 基于新工具包
(2020): g2pM：A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a New Open Benchmark Dataset
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