metadata

dataset_info:
  features:
    - name: id
      dtype: string
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
    - name: text
      dtype: string
    - name: segment_start_time
      dtype: float32
    - name: segment_end_time
      dtype: float32
    - name: duration
      dtype: float32
  splits:
    - name: test
      num_bytes: 140620332.25
      num_examples: 2822
    - name: train
      num_bytes: 608597323.625
      num_examples: 11291
  download_size: 711464914
  dataset_size: 749217655.875
tags:
  - audio
  - automatic-speech-recognition
  - en-atc
  - en
  - noisy-speech-recognition
  - speech-recognition
task_categories:
  - automatic-speech-recognition
language:
  - en
multilinguality:
  - monolingual
license:
  - cc-by-nc-sa-4.0

Dataset Card for UWB-ATCC corpus

Dataset Description
Dataset Structure
- Data Fields
Additional Information
- Licensing Information
- Citation Information

Dataset Description

Homepage: UWB-ATCC corpus homepage
Repository: GitHub repository (used in research)
Paper: Air traffic control communication (ATCC) speech corpora and their use for ASR and TTS development
Paper of this research: How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications

Dataset Summary

The UWB-ATCC Corpus is provided provided by University of West Bohemia, Department of Cybernetics. The corpus contains recordings of communication between air traffic controllers and pilots. The speech is manually transcribed and labeled with the information about the speaker (pilot/controller, not the full identity of the person). The corpus is currently small (20 hours) but we plan to search for additional data next year. The audio data format is: 8kHz, 16bit PCM, mono.

Important, from the <id (string)> field, you can obtain the speaker roles. For instance:

_PI: segment with only pilot speech
_AT: segment with only ATCO speech
PIAT: segment with both, ATCO and pilot speech

Supported Tasks and Leaderboards

automatic-speech-recognition. Already adapted/fine-tuned models are available here --> XLS-R-300m.

Languages and other details

The text and the recordings are in English. The authors took advantage of the fact that one of their industrial partners develops complex IT solutions for several ATC authorities and airports and, as such, has access to the ATC communication recordings collected in the Czech airspace. This partner was able to secure the following data:

Ground control—communication before takeoff and after landing—19.2 h of data.
Tower control—communication during takeoff, landing and landing standby—22.5 h.
Approach control—communication during landing approach—25.5 h.
Area control—communication during overflights and cruises—71.3 h.

(Not all data is released. Check their website here)

Dataset Structure

Data Fields

id (string): a string of recording identifier for each example, corresponding to its.
audio (audio): audio data for the given ID
text (string): transcript of the file already normalized. Follow these repositories for more details w2v2-air-traffic and bert-text-diarization-atc
segment_start_time (float32): segment start time (normally 0)
`segment_end_time (float32): segment end time
duration (float32): duration of the recording, compute as segment_end_time - segment_start_time

Additional Information

Licensing Information

The licensing status of the dataset hinges on the legal status of the UWB-ATCC corpus creators.

They used Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) licensing.

Citation Information

Contributors who prepared, processed, normalized and uploaded the dataset in HuggingFace:

@article{zuluaga2022how,
    title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications},
    author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others},
    journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
    year={2022}
  }

@article{zuluaga2022bertraffic,
  title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications},
  author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others},
  journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
  year={2022}
  }

@article{zuluaga2022atco2,
  title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications},
  author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others},
  journal={arXiv preprint arXiv:2211.04054},
  year={2022}
}

Authors of the dataset:

@article{vsmidl2019air,
  title={Air traffic control communication (ATCC) speech corpora and their use for ASR and TTS development},
  author={{\v{S}}m{\'\i}dl, Lubo{\v{s}} and {\v{S}}vec, Jan and Tihelka, Daniel and Matou{\v{s}}ek, Jind{\v{r}}ich and Romportl, Jan and Ircing, Pavel},
  journal={Language Resources and Evaluation},
  volume={53},
  number={3},
  pages={449--464},
  year={2019},
  publisher={Springer}
}

Datasets:

Jzuluaga
/

uwb_atcc