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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 f...
The paper introduces UGTPhon, a grapheme-to-phoneme benchmark for user‑generated text in English, Vietnamese, and Korean, and presents a taxonomy for diagnosing pronunciation errors. It shows that existing G2P models and large language models struggle with canonical‑to‑non‑canonical text, with errors up to 66.8 PER points. A compositional G2P approach that uses exact‑match lookup and staged decoding reduces these errors and performs competitively with larger few‑shot LLMs.
The paper introduces a context‑aware neural grapheme‑to‑phoneme (G2P) system for unsegmented languages like Japanese, using a discriminative conditional random field over a word lattice built from dictionaries. It addresses data scarcity by generating over two million sentences with large language models. Experiments show the method surpasses traditional morphological analyzers and neural sequence models, achieving 99.62% target word reading accuracy and very low phoneme error rates on the Joyo‑Kanji‑Yomi benchmark.
arXiv:2610.20279v1 Announce Type: new Abstract: Part-of-speech tagging is a fundamental sequence labeling task and an important component for many downstream natural language processing applications....
arXiv:2609.13615v1 Announce Type: new Abstract: For our submission to the WMT26 Creole Language Translation Shared Task, we focus on machine translation (MT) models for Pacific creoles: Tok Pisin, Bi...
The paper introduces a context‑aware neural grapheme‑to‑phoneme (G2P) system for unsegmented languages like Japanese, combining a discriminative conditional random field over a dictionary‑based word lattice with large language model‑generated training data. By generating over two million synthetic sentences, the method addresses data scarcity and achieves superior performance compared to traditional morphological analyzers and neural sequence models. On the Joyo‑Kanji‑Yomi benchmark, it attains 99.62% target word reading accuracy, 0.32% target word phoneme error rate, and 0.14% sentence phoneme error rate.