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: 471628915.76
      num_examples: 1901
    - name: train
      num_bytes: 1934757106.88
      num_examples: 7638
  download_size: 0
  dataset_size: 2406386022.6400003
tags:
  - audio
  - automatic-speech-recognition
  - en-atc
  - en
  - robust-speech-recognition
  - noisy-speech-recognition
  - speech-recognition
task_categories:
  - automatic-speech-recognition
language:
  - en
multilinguality:
  - monolingual

Dataset Card for ATCOSIM corpus

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

Dataset Description

Homepage: ATCOSIM homepage
Repository: GitHub repository (used in research)
Paper: The ATCOSIM Corpus of Non-Prompted Clean Air Traffic Control Speech
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 ATCOSIM Air Traffic Control Simulation Speech corpus is a speech database of air traffic control (ATC) operator speech, provided by Graz University of Technology (TUG) and Eurocontrol Experimental Centre (EEC). It consists of ten hours of speech data, which were recorded during ATC real-time simulations using a close-talk headset microphone. The utterances are in English language and pronounced by ten non-native speakers. The database includes orthographic transcriptions and additional information on speakers and recording sessions. It was recorded and annotated by Konrad Hofbauer (description here).

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 participating controllers were all actively employed air traffic controllers and possessed professional experience in the simulated sectors. The six male and four female controllers were of either German or Swiss nationality and had German, Swiss German or Swiss French native tongue. The controllers had agreed to the recording of their voice for the purpose of language analysis as well as for research and development in speech technologies, and were asked to show their normal working behaviour.

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 ATCOSIM corpus creators.

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:

@inproceedings{hofbauer-etal-2008-atcosim,
    title = "The {ATCOSIM} Corpus of Non-Prompted Clean Air Traffic Control Speech",
    author = "Hofbauer, Konrad  and
      Petrik, Stefan  and
      Hering, Horst",
    booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)",
    month = may,
    year = "2008",
    address = "Marrakech, Morocco",
    publisher = "European Language Resources Association (ELRA)",
    url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/545_paper.pdf",
}

Datasets:

Jzuluaga
/

atcosim_corpus