# Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications

arXiv: https://arxiv.org/abs/2502.20311 · HTML: https://mlsanity.com/paper/2502.20311/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 61 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 23 of 23)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Domain Adversarial Training for Accented Speech Recognition](https://mlsanity.com/paper/1806.02786/) (2018)
- [The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR](https://mlsanity.com/paper/2303.18110/) (2023)
- [How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR](https://mlsanity.com/paper/2201.06685/) (2022)
- [Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization](https://mlsanity.com/paper/2304.14535/) (2023)
- [ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications](https://mlsanity.com/paper/2211.04054/) (2022)
- [Automatic Speech Recognition Benchmark for Air-Traffic Communications](https://mlsanity.com/paper/2006.10304/) (2020)
- [Performance evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward](https://mlsanity.com/paper/2411.03866/) (2024)
- [Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data](https://mlsanity.com/paper/2205.08014/) (2022)
- [The Airbus Air Traffic Control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detection](https://mlsanity.com/paper/1810.12614/) (2018)
- [Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning](https://mlsanity.com/paper/2109.07349/) (2021)
- [Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings](https://mlsanity.com/paper/2110.03520/) (2021)
- [Do Prompts Really Prompt? Exploring the Prompt Understanding Capability of Whisper](https://mlsanity.com/paper/2406.05806/) (2024)
- [Supervised Contrastive Learning for Accented Speech Recognition](https://mlsanity.com/paper/2107.00921/) (2021)
- [ATCSpeech: a multilingual pilot-controller speech corpus from real Air Traffic Control environment](https://mlsanity.com/paper/1911.11365/) (2019)
- [Improving Accented Speech Recognition with Multi-Domain Training](https://mlsanity.com/paper/2303.07924/) (2023)
- [Accented Speech Recognition With Accent-specific Codebooks](https://mlsanity.com/paper/2310.15970/) (2023)
- [Toward Zero Oracle Word Error Rate on the Switchboard Benchmark](https://mlsanity.com/paper/2206.06192/) (2022)
- [Complexity and mission computability of adaptive computing systems](https://mlsanity.com/paper/1808.09586/) (2018)
- [Optimization problems with low SWaP tactical Computing](https://mlsanity.com/paper/1902.05070/) (2019)
- [ROSE: A Recognition-Oriented Speech Enhancement Framework in Air Traffic Control Using Multi-Objective Learning](https://mlsanity.com/paper/2312.06118/) (2023)
- [Understanding Realistic Attacks on Airborne Collision Avoidance Systems](https://mlsanity.com/paper/2010.01034/) (2020)
- [Enhancing multilingual speech recognition in air traffic control by sentence-level language identification](https://mlsanity.com/paper/2305.00170/) (2023)

## Built on by (most cited first, 3 of 3)

- [Safety-Oriented Evaluation of Language Understanding Systems for Air Traffic Control](https://mlsanity.com/paper/2605.11769/) (2026)
- [Synthetic Audio Generation Framework for Air Traffic Control Speech Recognition](https://mlsanity.com/paper/2606.21340/) (2026)
- [Beyond Semantic Accuracy: Consequence-Aware Evaluation for Safety-Critical Language Understanding](https://mlsanity.com/paper/2608.24621/) (2026)

## Abstract (arXiv)

Effective communication in Air Traffic Control (ATC) is critical to maintaining aviation safety, yet the challenges posed by accented English remain largely unaddressed in Automatic Speech Recognition (ASR) systems. Existing models struggle with transcription accuracy for Southeast Asian-accented (SEA-accented) speech, particularly in noisy ATC environments. This study presents the development of ASR models fine-tuned specifically for Southeast Asian accents using a newly created dataset. Our research achieves significant improvements, achieving a Word Error Rate (WER) of 0.0982 or 9.82% on SEA-accented ATC speech. Additionally, the paper highlights the importance of region-specific datasets and accent-focused training, offering a pathway for deploying ASR systems in resource-constrained military operations. The findings emphasize the need for noise-robust training techniques and region-specific datasets to improve transcription accuracy for non-Western accents in ATC communications.