G2P Shrinks Speech Models

The article explores the concept of Grapheme-to-Phoneme (G2P) conversion as a method for compressing speech models, discussing its potential benefits in reducing both model and dataset sizes. It proposes that by preprocessing text inputs into phonemes, text-to-speech (TTS) models can achieve similar performance with fewer parameters. The article contrasts heavyweight models like Parakeet and Llasa, which use large datasets and parameters, with featherweight models like Piper that utilize G2P preprocessing for efficiency. Various G2P methodologies, including lookup, rules, and neural approaches, are examined for their speed and generalization capabilities. The article notes challenges such as language-specific implementations and potential errors in G2P conversion, while suggesting that smaller models will remain relevant until technological advancements allow larger models to operate efficiently on more compact devices. The discussion includes a hybrid G2P approach to balance performance and flexibility, acknowledging that G2P is not without its limitations and may not fully replicate the expressiveness of end-to-end models.