Measuring benchmark optimization in speech recognition

Research on 11 open-source automatic speech recognition models finds that high scores on public benchmarks such as VoxPopuli and LibriSpeech can partly reflect benchmark-specific optimization rather than general transcription ability. Using three probes—reference disagreement, masked entity retrieval, and orthographic switching—the researchers observed that several leading models reproduced known reference-transcript errors, supplied numbers that had been silenced from audio, and selected dataset-specific spellings for phonetically identical words. These effects often weakened or disappeared when the same content was synthesized in generic voices or evaluated on newly collected recordings from similar domains, suggesting that models may use acoustic cues to identify benchmark data and follow its expected transcription conventions. The study reports that potential transcription errors appeared in 40% of analyzed VoxPopuli clips, while models with lower benchmark word error rates were often more likely to reproduce incorrect references. The authors recommend fully held-out evaluations, broader measures beyond single benchmark word error rate, improved dataset splits based on time or speakers, and greater transparency about training and model-selection data to distinguish real progress from benchmark fitting.

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