Grapheme to Phoneme

Grapheme-to-phoneme (G2P) conversion aims to translate written text into its phonetic representation, a crucial step in speech technology. Current research focuses on improving G2P accuracy and robustness using various approaches, including large language models (LLMs), transformer-based architectures, and data-driven methods that reduce reliance on handcrafted lexicons. These advancements are significantly impacting speech synthesis and recognition systems, particularly for low-resource languages and applications requiring accurate pronunciation of rare or context-dependent words. The development of more efficient and accurate G2P models is driving progress in numerous fields, including text-to-speech, speech recognition, and even the analysis of neurodegenerative diseases.