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arXiv · 2604.07354

Contextual Earnings-22: A Speech Recognition Benchmark with Custom Vocabulary in the Wild

Berkin Durmus·Chen Cen·Eduardo Pacheco·Arda Okan·Atila Orhon
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Abstract

The accuracy frontier of speech-to-text systems has plateaued on academic benchmarks.1 In contrast, industrial benchmarks and adoption in high-stakes domains suggest otherwise. We hypothesize that the primary difference between the two is contextual conditioning: Academic benchmarks are dominated by frequently encountered general vocabulary that is relatively easy to recognize compared with rare and context-defined custom vocabulary that has disproportionate impact on the usability of speech transcripts. Despite progress on contextual speech-to-text, there is no standardized benchmark. We introduce Contextual Earnings-22, an open dataset built upon Earnings-22, with realistic custom vocabulary contexts to foster research and reveal latent progress. We set six strong baselines for two dominant approaches: keyword prompting and keyword boosting. Experiments show both reach comparable and significantly improved accuracy when scaled from proof-of-concept to large-scale systems.

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Berkin Durmus, Chen Cen, Eduardo Pacheco, Arda Okan, Atila Orhon. 2026-03-28. Contextual Earnings-22: A Speech Recognition Benchmark with Custom Vocabulary in the Wild. https://arxiv.org/abs/2604.07354

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