🤖 AI Summary
This study addresses the challenge of achieving robust phonetic transcription in low-resource scenarios involving non-standard dialects and atypical speech—such as non-native and post-stroke dysarthric utterances—where expert phonetic annotation is prohibitively expensive. The authors systematically investigate the interplay between grapheme-to-phoneme (G2P) models and human supervision, revealing on an 80-hour multi-variety speech benchmark that G2P augmentation benefits performance only when fewer than 20–30 hours of manual transcriptions are available; beyond this threshold, it degrades cross-dialect generalization. To overcome this limitation, they propose replacing G2P with ASR-based pretraining and introduce a weighted phonetic feature error rate for evaluation. This approach yields substantial gains on both non-native and aphasic speech, reducing error rates by a factor of 2.3 compared to prior systems.
📝 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.