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arXiv — NLP / Computation & Language · · 3 min read

Earnings25: A Comprehensive 500-Hour Speech Benchmark for Finance

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Computer Science > Computation and Language

arXiv:2607.23813 (cs)
[Submitted on 26 Jul 2026]

Title:Earnings25: A Comprehensive 500-Hour Speech Benchmark for Finance

View a PDF of the paper titled Earnings25: A Comprehensive 500-Hour Speech Benchmark for Finance, by Denglin Jiang and 9 other authors
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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.
Comments: 5 pages, 0 figures, 5 tables
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
ACM classes: I.2.7
Cite as: arXiv:2607.23813 [cs.CL]
  (or arXiv:2607.23813v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2607.23813
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Denglin Jiang [view email]
[v1] Sun, 26 Jul 2026 19:24:00 UTC (34 KB)
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