Year: 2024
Type: conference-paper
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 linguisticallymotivated 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 competitive results with a 31.86%Word Error Rate on a standard benchmark, while generating linguistically meaningful grapheme segmentations.
Authors: Samuel Rose, Nina Dethlefs, C. Kambhampati
Institution: University of Hull
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404784183