Towards Quantifying Benchmark Optimization in ASR Models
Quick summary
arXiv:2608.19936v1 Announce Type: cross Abstract: 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: refe
Key takeaways
- arXiv:2608.19936v1 Announce Type: cross Abstract: 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.
Why it matters
“Towards Quantifying Benchmark Optimization in ASR Models” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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