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Filipino G2P model achieves high accuracy using ByT5 and LLM-assisted data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method for stress-aware grapheme-to-phoneme (G2P) conversion for the Filipino language at the sentence level. This approach addresses the challenge of limited sentence-level phoneme data by fine-tuning a ByT5 model using an LLM-assisted pipeline and data from Wiktionary. The resulting models show significant improvements in G2P accuracy, achieving a word error rate of around 0.54% and a character error rate of 2.50% on a corrected test set, and demonstrate potential for Filipino homograph disambiguation. AI

IMPACT This research advances natural language processing capabilities for Filipino, potentially improving speech synthesis and recognition systems.

RANK_REASON The cluster contains an academic paper detailing a new method for G2P conversion. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Filipino G2P model achieves high accuracy using ByT5 and LLM-assisted data

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The cluster contains an academic paper detailing a new method for G2P conversion. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Lorenz Bernard Marqueses, Paulo Grane Gabriel Silva, Chastine Cabatay, Ericson Adler Tan, Ann Franchesca Laguna ·

    Towards Stress-Aware Sentence-Level Filipino G2P With Weakly-Supervised ByT5 Fine-Tuning

    arXiv:2609.09974v1 Announce Type: new Abstract: Grapheme-to-phoneme conversion (G2P) refers to the task of converting a sequence of graphemes to a corresponding sequence of phonemes. While Filipino G2P is fairly straightforward due to its shallow orthography, the inclusion of pro…

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