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

2026-09-22

AI Summary

研究解决了用户生成文本的发音问题，通过引入UGTPhon基准和提出结合规范形式证据的组合方法来改进图素到音素转换模型。

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.

Problem

• Text-to-speech
• User-Generated Text
• Grapheme-to-Phoneme
• Canonical Form
• Performance Gap

Innovation

• UGTPhon
• Grapheme-to-Phoneme (G2P)
• Canonical Form Inference
• Compositional G2P Approach
• User-Generated Text