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New Earnings25 benchmark evaluates ASR for financial earnings calls

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Earnings25, a new benchmark designed to evaluate automatic speech recognition (ASR) systems specifically for financial earnings calls. This benchmark includes a substantial dataset of nearly 500 hours of S&P 500 earnings calls from Q4 2025, along with a segmented set of 46 hours representing various industries. Earnings25 offers aligned transcripts and metadata such as speaker roles and industry labels to facilitate more nuanced evaluations beyond simple word error rate. AI

IMPACT Enables more accurate evaluation of ASR systems in the finance sector, potentially improving tools for financial analysis and reporting.

RANK_REASON The item describes a new academic benchmark for evaluating AI systems, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Earnings25 benchmark evaluates ASR for financial earnings calls

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The item describes a new academic benchmark for evaluating AI systems, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Denglin Jiang, Haoran Zhou, Anshul Wadhawan, Brendan Fahy, Vinay Ramesh, David Weisberg, Dmitriy Derkachevskiy, Helen Sheehan, Srivas Prasad, Michele Franceschini ·

    Earnings25: A Comprehensive 500-Hour Speech Benchmark for Finance

    arXiv:2607.23813v1 Announce Type: cross 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-ful…

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