Towards Quantifying Benchmark Optimization in ASR Models
Public benchmarks are important measures of Automatic Speech Recognition (ASR) model capabilities. However, by nature of being public, there is risk of models being optimized for these benchmarks in ways that do not generalize well to real-world data. We present a methodology for quantifying benchmark optimization, focusing on cases where the audio underdetermines the reference transcript. We identify three families of behavioral probes that reveal models' capabilities of reproducing benchmark reference spans despite underdetermined audio: reference disagreement, masked-number recovery, and orthographic switching. We find that the highest-scoring open source models output verbatim referen...
| Authors | Theo Lebryk, David Ayllon, Alice Baird et al. |
|---|---|
| Primary category | cs.SD |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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