Text-to-speech systems increasingly process user-generated text (UGT) such as ppl and imo, whose pronunciation must be inferred from the canonical rather than surface form. We introduce UGTPHON, the first grapheme-to-phoneme (G2P) benchmark for UGT in English, Vietnamese, and Korean, with an inference-grounded taxonomy. Existing G2P models and frontier LLMs share a systematic failure pattern, with the canonical-to-non-canonical gap reaching up to 66.8 PER points. We propose a retrieval-augmented G2P approach that explicitly models canonical-form inference through retrieval and staged decoding. Under 0.5B parameters, our approach shrinks this gap by up to 83% while outperforming most frontier LLMs that are orders of magnitude larger, demonstrating that explicit compositional structure can substitute for scale.