# Synthetic Audio Generation Framework for Air Traffic Control Speech Recognition

arXiv: https://arxiv.org/abs/2606.21340 · HTML: https://mlsanity.com/paper/2606.21340/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 34 references that are in the map.
- First posted on arXiv 19 Jun 2026.

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

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching](https://mlsanity.com/paper/2410.06885/) (2024)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Voice Conversion With Just Nearest Neighbors](https://mlsanity.com/paper/2305.18975/) (2023)
- [AudioSR: Versatile Audio Super-resolution at Scale](https://mlsanity.com/paper/2309.07314/) (2023)
- [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://mlsanity.com/paper/2203.16822/) (2022)
- [Parrotron: An End-to-End Speech-to-Speech Conversion Model and its Applications to Hearing-Impaired Speech and Speech Separation](https://mlsanity.com/paper/1904.04169/) (2019)
- [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)
- [Voice Conversion Can Improve ASR in Very Low-Resource Settings](https://mlsanity.com/paper/2111.02674/) (2021)
- [The Airbus Air Traffic Control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detection](https://mlsanity.com/paper/1810.12614/) (2018)
- [AccentBox: Towards High-Fidelity Zero-Shot Accent Generation](https://mlsanity.com/paper/2409.09098/) (2024)
- [Non-native children speech recognition through transfer learning](https://mlsanity.com/paper/1809.09658/) (2018)
- [Improving speech recognition models with small samples for air traffic control systems](https://mlsanity.com/paper/2102.08015/) (2021)
- [Accent conversion using discrete units with parallel data synthesized from controllable accented TTS](https://mlsanity.com/paper/2410.03734/) (2024)
- [Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data](https://mlsanity.com/paper/2507.17735/) (2025)
- [Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications](https://mlsanity.com/paper/2502.20311/) (2025)
- [Non-Parallel Voice Conversion for ASR Augmentation](https://mlsanity.com/paper/2209.06987/) (2022)
- [DAIEN-TTS: Disentangled Audio Infilling for Environment-Aware Text-to-Speech Synthesis](https://mlsanity.com/paper/2509.14684/) (2025)
- [BEST-RQ-Based Self-Supervised Learning for Whisper Domain Adaptation](https://mlsanity.com/paper/2510.24570/) (2025)
- [In-domain SSL pre-training and streaming ASR](https://mlsanity.com/paper/2509.12101/) (2025)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) systems, despite achieving remarkable accuracy in general-purpose domains with native speech (L1), struggle in domains like Air Traffic Control (ATC) due to strong channel noise, a presence of non-native (L2) English accents, and data scarcity. We propose a synthetic data generation pipeline with acoustical properties simulations specifically designed to address this lack of real data to improve recognition accuracy in the ATC domain. Our approach leverages a combination of neural generation techniques, including Text-to-Speech, Voice Conversion, L2-to-L1 accent conversion, and a novel controllable L1-to-L2 accent conversion framework built to simulate accented speech. Our experiments with the Whisper model on the ATCO2 corpus demonstrate that fine-tuning with either synthetic data alone, or a mix of real and synthetic data, significantly improves the word error rate over out-of-the-box and real data only baselines respectively.