# A Survey of Graphene-to-Phoneme Conversion Methods
## 2 Grapheme-to-Phoneme Methods
### Traditional Methods  
#### 2.1.1 Rule-Based and Dictionary-Based Methods  
The simplest method for grapheme-to-phoneme conversion is dictionary lookup, but constructing a large dictionary is both expensive and time-consuming, and there will always be words outside the vocabulary. Therefore, rule-based methods were proposed to address the issues with dictionary lookups. Rule-based grapheme-to-phoneme conversion is a traditional language processing technique that requires mapping the graphemes (shapes and arrangements of letters) to their corresponding phonemes (the smallest units of speech) through predefined sets of rules. This method typically relies on the in-depth understanding of language structure and pronunciation rules by linguists or domain experts. By summarizing the spelling and pronunciation rules of a language, phonetic conversion rules are manually formed to predict the pronunciation of words. Developing such a grapheme-to-phoneme system requires the use of numerous grapheme-to-phoneme rules  
Figure 1: Overall structure of this survey.  
Figure 2: Commonly employed grapheme-to-phoneme conversion methods.  
and expert knowledge. For certain languages (such as Chinese and Japanese) with complex writing systems, constructing rules requires a significant amount of human effort and is costly, and it is difficult to cover most possible scenarios. The literature [1] proposed a rule-based G2P system, formulating a set of 329 grapheme-to-sound rules to convert English text into the International Phonetic Alphabet (IPA). The literature [2] studied mathematical and computational tools for reasoning about regular languages and regular relations, proposing several rule-based methods. However, some languages (such as Chinese and Japanese) have very complex writing systems, requiring a significant amount of human effort to construct grapheme-to-phoneme rules, and there is no guarantee that the dictionary will contain all words in the text.  
The literature [3] used a hand-seeded algorithm to achieve optimal alignment, then employed decision tree technology to implement a general framework for building letter-to-sound (LTS) rules from a list of words in a language, successfully constructing LTS models for four different languages. This method can remove regular examples from the word list, allowing even smaller dictionaries to cover all words. The model's performance in handling unknown words was evaluated in detail through the conversion results of unknown words in news texts. The model can generate reasonable pronunciations for unseen words, with errors mainly concentrated on proper nouns, especially those from foreign languages.  
The literature [4] proposed a method to represent the complexity of languages to guide developers in addressing the question of \"how to use words to learn rules\", designing a word selection strategy that outperforms random selection. Moreover, the introduction of knowledge about word alignment and rule order errors can further improve this strategy. However, this method is not suitable for languages with unclear connections between graphemes and phonemes, as the number of grapheme-to-phoneme conversion rules in these languages almost grows linearly with the size of the dictionary.