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arXiv · 2607.23813

Earnings25: A Comprehensive 500-Hour Speech Benchmark for Finance

Denglin Jiang·Haoran Zhou·Anshul Wadhawan·Brendan Fahy·Vinay Ramesh·David Weisberg·Dmitriy Derkachevskiy·Helen Sheehan·Srivas Prasad·Michele Franceschini
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Abstract

We introduce Earnings25, a finance-domain benchmark for evaluating automatic speech recognition (ASR) on English-language earnings calls under realistic conditions. Earnings25 comprises two complementary test sets: (i) testset-full, 498 hours of full English-language S&P 500 earnings calls from Q4 2025, and (ii) testset-segmented, a 46-hour industry-balanced set of 290 segments sampled from English-language U.S. earnings calls in 2025. The benchmark provides aligned transcripts and structured metadata, including speaker roles, industry labels, and call structure, enabling speaker- and industry-aware evaluation beyond aggregate word error rate (WER). We report reproducible baselines for Whisper and Parakeet-TDT using standardized scoring.

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Denglin Jiang, Haoran Zhou, Anshul Wadhawan, Brendan Fahy, Vinay Ramesh, David Weisberg, Dmitriy Derkachevskiy, Helen Sheehan, Srivas Prasad, Michele Franceschini. 2026-07-26. Earnings25: A Comprehensive 500-Hour Speech Benchmark for Finance. https://arxiv.org/abs/2607.23813

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