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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

—

ISSN

2504-3900

e-ISSN

—

Počet stran výsledku

10

Strana od-do

1-10

Název nakladatele

MDPI

Místo vydání

Brussels

Místo konání akce

EUROCONTROL in Brussels, Belgium

Datum konání akce

18. 11. 2021

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—