One-Vs-Rest Neural Network E nglish Grapheme Segmentation: A Linguistic Perspective (2024)

Abstract: Grapheme-to-Phoneme (G2P) correspondences form foundational frameworks of tasks such as text-to-speech (TTS) synthesis or automatic speech recognition. The G2P process involves taking words in their written form and generating their pronunciation. In this paper, we critique the status quo definition of a grapheme, currently a forced alignment process relating a single character to either a phoneme or a blank unit, that underlies the majority of modern approaches. We develop a linguistically-motivated redefinition from simple concepts such as vowel and consonant count and word length and offer a proof-of-concept implementation based on a multi-binary neural classification task. Our model achieves state-of-the-art results with a 31.86% Word Error Rate on a standard benchmark, while generating linguistically meaningful grapheme segmentations.

## introduction segmenting words into graphemes is crucial for accurate and reliable text-to-speech systems @xcite @xcite , as well as providing a tokenisation framework for training language models for use by varied segments of society @xcite . the currently predominant approach to g2p, which extracts phonemes from a list of graphemes, is one of forced alignment @xcite @xcite . in this approach, a grapheme is defined as a single character that either does or does not have a respective phoneme when using g2p correspondences. this process is illustrated in the contributions of this paper are as follows: ## linguistic definitions of graphemes in nlp areas, a grapheme is currently defined as a single character, with g2p models aligning each character with a phoneme or a blank unit. outside of nlp research, there are two linguistic theories on graphemes. referential conception @xcite defines a grapheme as the smallest written unit corresponding with phonemes, like "ph" in "phonetics" for the /f/ phoneme. this theory suggests writing depicts speech. the analogical concept @xcite uses minimal pairs to show phoneme differences based on spelling, such as "t" and "k" in "parts" and "parks," arguing that writing and speech should be studied separately. g2p correspondences balance these theories by viewing graphemes as influencing pronunciation but also as distinct from phonemes in tts research. this hybrid approach presents challenges. given the focus on tts in g2p models, we propose adopting the referential conception for computational linguistic applications as in these applications, writing is being used to mimic and create spoken language. we rely on @xcite , who conducted a de-tailed analysis of british english spelling, identifying 284 graphemes: 89 in the 'main system' and 195 in the 'extended system,' corresponding to 43 phonemes. brooks notes that while the number of graphemes remains the same in american english, correspondences differ to reflect pronunciation differences. ## conclusion our redefinition of graphemes, inspired by the referential conception theory, has profound implications for the task of g2p. already approaching results given in state-of-the-art methods using a simple architecture, our research challenges current methodologies, highlighting the limitations of single-character graphemes, and offering a more inclusive framework for text representation and semantic research. this shift paves the way for more accurate, culturally-sensitive language processing systems. this paper advances nlp research by advocating for hybrid graphemes, addressing critical gaps in existing methods. it provides practitioners with tools to improve the performance and adaptability of their applications, and encourages exploration of the phonetics-semantics connection, influencing text tokenisation, segmentation, and feature extraction in nlg applications. additionally, the application of hybrid graphemes will aid in speech recognition tasks, such as differentiating homophones, and modelling dialect differences in english, reflecting true linguistic diversity and additionally allowing for more culturally sensitive models.