G2P Model

Grapheme-to-phoneme (G2P) conversion aims to automatically translate written text into its phonetic representation, a crucial step in text-to-speech systems and other speech technologies. Recent research emphasizes developing data-driven, lexicon-free G2P models using architectures like Transformers and Byte-Pair Encoding (BPE), overcoming limitations of traditional rule-based and lexicon-dependent approaches. Current efforts focus on improving model robustness to noisy or varied input, handling sentence-level context for improved accuracy, and creating multilingual models capable of handling a wide range of languages, even those with limited resources. These advancements are significant for improving the accessibility and quality of speech technologies across diverse languages and applications.