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arXiv · 2509.07195

Identifying and Calibrating Overconfidence in Noisy Speech Recognition

Mingyue Huo·Yuheng Zhang·Yan Tang
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Abstract

Modern end-to-end automatic speech recognition (ASR) models like Whisper not only suffer from reduced recognition accuracy in noise, but also exhibit overconfidence - assigning high confidence to wrong predictions. We conduct a systematic analysis of Whisper's behavior in additive noise conditions and find that overconfident errors increase dramatically at low signal-to-noise ratios, with 10-20% of tokens incorrectly predicted with confidence above 0.7. To mitigate this, we propose a lightweight, post-hoc calibration framework that detects potential overconfidence and applies temperature scaling selectively to those tokens, without altering the underlying ASR model. Evaluations on the R-SPIN dataset demonstrate that, in the low signal-to-noise ratio range (-18 to -5 dB), our method reduces the expected calibration error (ECE) by 58% and triples the normalized cross entropy (NCE), yielding more reliable confidence estimates under severe noise conditions.

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Mingyue Huo, Yuheng Zhang, Yan Tang. 2025-09-08. Identifying and Calibrating Overconfidence in Noisy Speech Recognition. https://arxiv.org/abs/2509.07195

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