Earnings25: A Comprehensive 500-Hour Speech Benchmark for Finance
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 26 Jul 2026.
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References · 9
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2019NeMo: a toolkit for building AI applications using Neural Modulescited by 230
- 2021SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognitioncited by 77
- 2021Earnings-21: A Practical Benchmark for ASR in the Wildcited by 60
- 2023Efficient Sequence Transduction by Jointly Predicting Tokens and Durationscited by 56
- 2022Earnings-22: A Practical Benchmark for Accents in the Wildcited by 46
- 2021NeMo Inverse Text Normalization: From Development To Productioncited by 28
- 2025SPGISpeech 2.0: Transcribed multi-speaker financial audio for speaker-tagged transcriptioncited by 5
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