Towards Stress-Aware Sentence-Level Filipino G2P With Weakly-Supervised ByT5 Fine-Tuning
Summary
This paper investigates weakly-supervised ByT5 fine-tuning for stress-aware sentence-level Filipino grapheme-to-phoneme conversion, achieving significant improvements in phoneme and character error rates on a manually-corrected test set.
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# Towards Stress-Aware Sentence-Level Filipino G2P With Weakly-Supervised ByT5 Fine-Tuning Source: [https://arxiv.org/abs/2609.09974](https://arxiv.org/abs/2609.09974) [View PDF](https://arxiv.org/pdf/2609.09974) > 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 prosodic features such as stress adds a layer of complexity that requires sentence\-level context instead of single\-word inputs\. However, sentence\-level data for Filipino typically do not include phoneme transcriptions, posing a challenge for training G2P models\. As such, we investigate how to obtain sentence\-level phoneme data for Filipino using available data and compare the resulting models with multilingual word\-level G2P as well as measure how accurately they predict stress marker position for Filipino\. We propose fine\-tuning a ByT5\-based model, pre\-trained on multilingual word\-level G2P data, on three sentence\-level G2P datasets annotated with an LLM\-assisted pipeline guided by data from Wiktionary\. This approach produces models that perform well on the G2P task, achieving at best around 0\.54% PER and 2\.50% CER, a significant decrease compared to base model PER at around 19\.74%, on a manually\-corrected test set\. The model is able to correctly classify most of the main stress classes in Filipino, but struggles particularly with malumi words\. We show that a ByT5\-based model performs well at sentence\-level Filipino G2P and offers strong potential for Filipino homograph disambiguation\. ## Submission history From: Lorenz Bernard Marqueses \[[view email](https://arxiv.org/show-email/8ce27d06/2609.09974)\] **\[v1\]**Wed, 9 Sep 2026 10:02:25 UTC \(1,116 KB\)
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