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.

翻译：专家语音标注成本高昂，尤其在非标准方言及非典型语音场景中。常用的替代方案是采用字形-音素（G2P）模型从文本转录本中自动生成大规模音标。我们研究了英语自动语音转录性能随人类监督与G2P监督规模化的变化规律。基于一个涵盖母语、非母语及中风后语音的80小时精选基准测试，我们识别出监督质量阈值：仅在人工标注不足20-30小时时，G2P监督才有助益；超出该阈值后，其不仅无法带来显著提升，反而可能降低跨方言鲁棒性。在此阈值后有效的策略是ASR预训练——通过该方法，我们实现了加权音素特征错误率较先前系统降低2.3倍，尤其对非母语及失语症语音取得显著增益。这些结果表明，面向鲁棒泛化时，基于数据量驱动的G2P规模化可能产生边际效益递减。