# ATCSpeech: a multilingual pilot-controller speech corpus from real Air Traffic Control environment

arXiv: https://arxiv.org/abs/1911.11365 · HTML: https://mlsanity.com/paper/1911.11365/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 8 of 8)

- [Improving speech recognition models with small samples for air traffic control systems](https://mlsanity.com/paper/2102.08015/) (2021)
- [ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems](https://mlsanity.com/paper/2102.08535/) (2021)
- [A Comparative Study of Speaker Role Identification in Air Traffic Communication Using Deep Learning Approaches](https://mlsanity.com/paper/2111.02041/) (2021)
- [Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications](https://mlsanity.com/paper/2502.20311/) (2025)
- [Speech recognition for air traffic control via feature learning and end-to-end training](https://mlsanity.com/paper/2111.02654/) (2021)
- [ROSE: A Recognition-Oriented Speech Enhancement Framework in Air Traffic Control Using Multi-Objective Learning](https://mlsanity.com/paper/2312.06118/) (2023)
- [SCOPE: A Lightweight-training LLM Framework for Air Traffic Control Readback Monitoring](https://mlsanity.com/paper/2605.29543/) (2026)
- [Beyond Semantic Accuracy: Consequence-Aware Evaluation for Safety-Critical Language Understanding](https://mlsanity.com/paper/2608.24621/) (2026)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) is greatly developed in recent years, which expedites many applications on other fields. For the ASR research, speech corpus is always an essential foundation, especially for the vertical industry, such as Air Traffic Control (ATC). There are some speech corpora for common applications, public or paid. However, for the ATC, it is difficult to collect raw speeches from real systems due to safety issues. More importantly, for a supervised learning task like ASR, annotating the transcription is a more laborious work, which hugely restricts the prospect of ASR application. In this paper, a multilingual speech corpus (ATCSpeech) from real ATC systems, including accented Mandarin Chinese and English, is built and released to encourage the non-commercial ASR research in ATC domain. The corpus is detailly introduced from the perspective of data amount, speaker gender and role, speech quality and other attributions. In addition, the performance of our baseline ASR models is also reported. A community edition for our speech database can be applied and used under a special contrast. To our best knowledge, this is the first work that aims at building a real and multilingual ASR corpus for the air traffic related research.