# A Survey of Graphene-to-Phoneme Conversion Methods
## 1 Introduction  
Natural Language Processing (NLP) is a crucial branch of artificial intelligence aimed at enabling computers to understand, generate, and process human language. Text-to-speech (TTS) technology is a significant application of NLP and is widely used in voice assistants, accessibility communication, and other fields. In TTS systems, the graphene-to-phoneme (G2P) model converts text into phoneme sequences, directly impacting the quality of speech synthesis and determining whether TTS systems can achieve state-of-the-art performance levels. G2P is a language processing technology that maps graphemes (such as letters or letter combinations) in a writing system to their corresponding phonemes, the smallest units of speech. This technology converts letter strings like \(APPLE\) into phoneme strings like \([AEPAHL]\). Formally, let \(\Sigma G\) be the grapheme alphabet and \(\Sigma P\) be the phoneme alphabet. For a word \(w\) in a language, grapheme-to-phoneme refers to the mapping:  
\[g(w)\to p(w), \tag{1}\]  
where \(g(w)\in\Sigma_{G}^{*}\) and \(p(w)\in\Sigma_{p}^{*}\) are the grapheme and phoneme representations of \(w\), respectively. Graphene-to-phoneme conversion has a wide range of applications and plays a crucial role in text-to-speech and automatic speech recognition systems. The state-of-the-art performance of these systems depends heavily on the accuracy of the grapheme-to-phoneme conversion. In text-to-speech systems, a high-quality G2P model is an essential component that significantly impacts the system's quality. Inaccurate grapheme-to-phoneme conversion can lead to unnatural pronunciations and unintelligible speech. Given the inherent complexity of languages, the G2P task presents numerous challenges. Initially, themany-to-one and one-to-many mapping relationships between graphemes and phonemes, coupled with the contextual dependency of pronunciation, significantly augment the task's complexity. Subsequently, the pronounced disparities in spelling and pronunciation rules across various languages and dialects, which are exacerbated by the introduction of loanwords, further complicate the G2P conversion process. In the context of multilingual and low-resource language conversion tasks, G2P conversion within a multilingual framework necessitates the accommodation of the divergent spelling and phoneme systems of different languages, while low-resource languages grapple with issues of data scarcity. Numerous researchers have proposed many solutions for the grapheme-to-phoneme conversion task. From a developmental perspective, grapheme-to-phoneme conversion algorithms can be categorized into traditional rule-based and dictionary-based methods, statistical machine learning methods, and deep learning-based conversion methods. Very early grapheme-to-phoneme conversion systems were specifically designed for English, utilizing language-specific characteristics or domain knowledge to formulate rules aimed at addressing the variations within the English language. These systems required custom-tailored rules for individual languages, and their performance was largely confined to the language itself. Dictionary-based methods also had many limitations; even frequent updates to the dictionary could not guarantee coverage of all words in a given language. The combination of dictionaries and rules offered relatively better performance, whether they primarily used a dictionary with post-processing rules or primarily used rules for languages with regular pronunciations while only using dictionary lookups for exceptional cases. This traditional approach could accurately convert input text into corresponding phonemes. Overall, these methods achieved good recognition results in specific corpora, but they were costly to develop and could not be easily ported to other languages. Even in highly regular languages, there are special cases, especially for loanwords from foreign languages, where rule-based methods could lead to incorrect conversions.