Automatic Processing Pipeline for Collecting and Annotating Air-Traffic Voice Communication Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU143424" target="_blank" >RIV/00216305:26230/21:PU143424 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2673-4591/13/1/8/htm" target="_blank" >https://www.mdpi.com/2673-4591/13/1/8/htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/engproc2021013008" target="_blank" >10.3390/engproc2021013008</a>

Result language

angličtina

Original language name

Automatic Processing Pipeline for Collecting and Annotating Air-Traffic Voice Communication Data

Original language description

This document describes our pipeline for automatic processing of ATCO pilot audio communication we developed as part of the ATCO2 project. So far, we collected two thousand hours of audio recordings that we either preprocessed for the transcribers or used for semi-supervised training. Both methods of using the collected data can further improve our pipeline by retraining our models. The proposed automatic processing pipeline is a cascade of many standalone components: (a) segmentation, (b) volume control, (c) signal-to-noise ratio filtering, (d) diarization, (e) speech-totext (ASR) module, (f) English language detection, (g) call-sign code recognition, (h) ATCOpilot classification and (i) highlighting commands and values. The key component of the pipeline is a speech-to-text transcription system that has to be trained with real-world ATC data; otherwise, the performance is poor. In order to further improve speech-to-text performance, we apply both semisupervised training with our recordings and the contextual adaptation that uses a list of plausible callsigns from surveillance data as auxiliary information. Downstream NLP/NLU tasks are important from an application point of view. These application tasks need accurate models operating on top of the real speech-to-text output; thus, there is a need for more data too. Creating ATC data is the main aspiration of the ATCO2 project. At the end of the project, the data will be packaged and distributed by ELDA.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of 9th OpenSky Symposium 2020, OpenSky Network, Brussels, Belgium

ISBN

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ISSN

2504-3900

e-ISSN

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Number of pages

10

Pages from-to

1-10

Publisher name

MDPI

Place of publication

Brussels

Event location

EUROCONTROL in Brussels, Belgium

Event date

Nov 18, 2021

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

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