摘要：语音错误检测（phonetic error detection，PED）旨在识别音素级别的替换、删除和插入错误，对于教育及临床语音评估和干预至关重要。尽管语音模型在音素识别方面近期取得进展，但由于缺乏高质量评估数据，其能否可靠检测细粒度语音错误仍不明确。为填补这一空白，我们推出 PEDBench——首个覆盖从自然语音到受控语音连续谱系的 PED 基准。该基准包含儿童实际口语错误的真实录音（N=334）、由真人演员完成的脚本化错误发音（N=1,188），以及使用音素条件文本转语音生成的合成错误发音（N=1,083）。所有语音标注均由两名接受过语音学训练的标注员转录并核验。我们对 13 个先进语音模型的评估显示，即使在演员语音和合成语音这类声学变异较小的受控条件下，它们也无法可靠检测语音错误。模型的精确率同样偏低，经常将发音正确的音素错误分类为错误。我们在合成数据上微调的语音语言模型，在词级 PED 任务上优于商业和开源基线；不过，仿真到真实场景的领域差距依然存在。我们计划公开发布代码和复现方案，以及演员语音和合成语音数据集，以支持开发更准确的 PED 系统。

Abstract:Phonetic error detection (PED) aims to identify phoneme-level substitutions, deletions, and insertions and is essential for educational and clinical speech assessment and intervention. Despite recent advances in speech models for phoneme recognition, their ability to reliably detect fine-grained phonetic errors remains unclear because of a lack of high-quality evaluation data. To address this gap, we introduce PEDBench, the first PED benchmark spanning a continuum from natural to controlled speech. It comprises real-world recordings of spoken errors made by children (N=334), scripted error productions by human actors (N=1,188), and synthetic error productions generated using phone-conditioned text-to-speech (N=1,083). All phonetic annotations were transcribed and verified by two phonetically trained annotators. Our evaluation of 13 state-of-the-art speech models shows that they fail to reliably detect phonetic errors, even under controlled conditions with acted and synthetic speech, where acoustic variability is reduced. The models also exhibit low precision, frequently misclassifying correctly pronounced phonemes as errors. Our speech-language models, fine-tuned on synthetic data, outperform commercial and open-source baselines on word-level PED, although a sim-to-real domain gap remains. We plan to publicly release our code and recipes, along with our acted and synthetic datasets, to support the development of more accurate PED systems.