Scaling Human and G2P Supervision for Robust Phonetic Transcription

Abstract:Expert phonetic annotation is costly, especially for non-standard dialects and atypical speech. A common alternative is using Grapheme-to-Phoneme (G2P) models to auto-generate phonetic labels from text transcripts at scale. We study how automatic phonetic transcription performance scales with human and G2P supervision in English. Using a curated 80-hour benchmark spanning native, non-native and post-stroke speech, we identify a supervision quality threshold: G2P supervision helps only when fewer than 20-30 hours of human annotation are available. Beyond this threshold, it provides no significant benefit and can reduce cross-dialect robustness. What is effective after this threshold is ASR pretraining which we use to achieve a 2.3x reduction in weighted phone feature error rate over prior systems, with strong gains on non-native and aphasic speech. These results suggest that quantity-driven G2P scaling may yield diminishing returns for robust generalization.

Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD)

arXiv:2606.16019 [cs.CL]

(or arXiv:2606.16019v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.16019 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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