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arXiv AI By Theodore O. Cochran, Stephanie Dodson, Keith Nore

No Detectable Change in Side-Level WER from Prompt-Level Context: A Preregistered Ablation on a Production Oral-History Corpus

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The study examined whether providing full prompt-level context to a large multimodal model would improve speech transcription accuracy on a production oral‑history corpus. Using a preregistered within‑item paired ablation, the authors found that adding context did not produce a detectable change in side‑level word error rate (WER) for either gpt‑4o‑transcribe or gemini‑2.5‑flash. The results suggest that context alone may not be sufficient to enhance aggregate transcription accuracy, and that finer‑grained, sequence‑aligned metrics are needed to evaluate such mechanisms.

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 AI.

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