Phonemizing User-Generated Text: A Benchmark, Taxonomy, and Compositional Approach

arXiv:2609.27205 · cs.CL, cs.AI · Submitted 2026-09-23 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-23

Updated: 2026-09-23

Comments: Accepted in EMNLP 2026 Findings

Code: https://github.com/naver-ai/UGTPHON

License: http://creativecommons.org/licenses/by/4.0/

The gist: 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.

Terminology

Abstract

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, together with an inference-grounded taxonomy for fine-grained diagnosis. Existing G2P models and frontier LLMs exhibit a systematic canonical-to-non-canonical performance gap, reaching up to 66.8 PER points. As a benchmark baseline, we propose a simple compositional G2P approach that incorporates canonical-form evidence through exact-match lookup and staged decoding. Across matched ByT5 and Qwen2.5-0.5B backbones, explicit canonical-form modeling consistently reduces non-canonical G2P errors. The 0.5B variant also performs competitively with much larger few-shot frontier LLMs, highlighting the benefit of explicitly modeling canonical-form inference for UGT phonemization.

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