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arXiv Computation and Language By Zhonghao Shi, Zackary Rackauckas, Jasmine Yip, Savannah Halpern, Simon Campos, Lauren Brace, Emma Yang, Tiantian Feng, Xuan Shi, Shrikanth Narayanan, Teresa Satterfield, Ying Xu

PEDBench: Can Speech Models Detect Phonetic Errors?

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PEDBench is a new benchmark for phonetic error detection (PED) that includes natural recordings of children’s speech errors, scripted error productions by actors, and synthetic error productions generated via phone‑conditioned text‑to‑speech. The benchmark provides high‑quality phonetic annotations verified by trained annotators. Evaluation of 13 state‑of‑the‑art speech models shows that they struggle to reliably detect phonetic errors, even in controlled conditions, and often misclassify correct phonemes as errors. Fine‑tuned speech‑language models perform better on word‑level PED, but a sim‑to‑real domain gap persists.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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