# Earnings-22: A Practical Benchmark for Accents in the Wild

arXiv: https://arxiv.org/abs/2203.15591 · HTML: https://mlsanity.com/paper/2203.15591/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 2 of 2)

- [SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition](https://mlsanity.com/paper/2104.02014/) (2021)
- [Earnings-21: A Practical Benchmark for ASR in the Wild](https://mlsanity.com/paper/2104.11348/) (2021)

## Built on by (most cited first, 30 of 50)

- [AudioBench: A Universal Benchmark for Audio Large Language Models](https://mlsanity.com/paper/2406.16020/) (2024)
- [Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling](https://mlsanity.com/paper/2311.00430/) (2023)
- [mHuBERT-147: A Compact Multilingual HuBERT Model](https://mlsanity.com/paper/2406.06371/) (2024)
- [Voxtral](https://mlsanity.com/paper/2507.13264/) (2025)
- [Streaming Sequence-to-Sequence Learning with Delayed Streams Modeling](https://mlsanity.com/paper/2509.08753/) (2025)
- [Stateful Conformer with Cache-based Inference for Streaming Automatic Speech Recognition](https://mlsanity.com/paper/2312.17279/) (2023)
- [Open ASR Leaderboard: Towards Reproducible and Transparent Multilingual and Long-Form Speech Recognition Evaluation](https://mlsanity.com/paper/2510.06961/) (2025)
- [ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition](https://mlsanity.com/paper/2210.13352/) (2022)
- [Zero-shot Domain-sensitive Speech Recognition with Prompt-conditioning Fine-tuning](https://mlsanity.com/paper/2307.10274/) (2023)
- [Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond](https://mlsanity.com/paper/2502.03945/) (2025)
- [Updated Corpora and Benchmarks for Long-Form Speech Recognition](https://mlsanity.com/paper/2309.15013/) (2023)
- [MERaLiON-AudioLLM: Bridging Audio and Language with Large Language Models](https://mlsanity.com/paper/2412.09818/) (2024)
- [NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model](https://mlsanity.com/paper/2411.05945/) (2024)
- [Voxtral Realtime](https://mlsanity.com/paper/2602.11298/) (2026)
- [Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models](https://mlsanity.com/paper/2507.07877/) (2025)
- [Longer is (Not Necessarily) Stronger: Punctuated Long-Sequence Training for Enhanced Speech Recognition and Translation](https://mlsanity.com/paper/2409.05601/) (2024)
- [WhisperKit: On-device Real-time ASR with Billion-Scale Transformers](https://mlsanity.com/paper/2507.10860/) (2025)
- [How Much Context Does My Attention-Based ASR System Need?](https://mlsanity.com/paper/2310.15672/) (2023)
- [Drax: Speech Recognition with Discrete Flow Matching](https://mlsanity.com/paper/2510.04162/) (2025)
- [PROFASR-BENCH: A Benchmark for Context-Conditioned ASR in High-Stakes Professional Speech](https://mlsanity.com/paper/2512.23686/) (2025)
- [Whisper-CD: Accurate Long-Form Speech Recognition using Multi-Negative Contrastive Decoding](https://mlsanity.com/paper/2603.06193/) (2026)
- [Hijacking Large Audio-Language Models via Context-Agnostic and Imperceptible Auditory Prompt Injection](https://mlsanity.com/paper/2604.14604/) (2026)
- [IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages](https://mlsanity.com/paper/2606.19157/) (2026)
- [Beyond Levenshtein: Leveraging Multiple Algorithms for Robust Word Error Rate Computations And Granular Error Classifications](https://mlsanity.com/paper/2408.15616/) (2024)
- [On the Robust Approximation of ASR Metrics](https://mlsanity.com/paper/2502.12408/) (2025)
- [FinAudio: A Benchmark for Audio Large Language Models in Financial Applications](https://mlsanity.com/paper/2503.20990/) (2025)
- [SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models](https://mlsanity.com/paper/2507.19361/) (2025)
- [Whisfusion: Parallel ASR Decoding with Masked Diffusion](https://mlsanity.com/paper/2508.07048/) (2025)
- [Contextual Earnings-22: A Speech Recognition Benchmark with Custom Vocabulary in the Wild](https://mlsanity.com/paper/2604.07354/) (2026)
- [Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training](https://mlsanity.com/paper/2307.12498/) (2023)

## Abstract (arXiv)

Modern automatic speech recognition (ASR) systems have achieved superhuman Word Error Rate (WER) on many common corpora despite lacking adequate performance on speech in the wild. Beyond that, there is a lack of real-world, accented corpora to properly benchmark academic and commercial models. To ensure this type of speech is represented in ASR benchmarking, we present Earnings-22, a 125 file, 119 hour corpus of English-language earnings calls gathered from global companies. We run a comparison across 4 commercial models showing the variation in performance when taking country of origin into consideration. Looking at hypothesis transcriptions, we explore errors common to all ASR systems tested. By examining Individual Word Error Rate (IWER), we find that key speech features impact model performance more for certain accents than others. Earnings-22 provides a free-to-use benchmark of real-world, accented audio to bridge academic and industrial research.