Automatic Speech Recognition Benchmark for Air-Traffic Communications
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU138640" target="_blank" >RIV/00216305:26230/20:PU138640 - isvavai.cz</a>
Result on the web
<a href="https://isca-speech.org/archive/Interspeech_2020/pdfs/2173.pdf" target="_blank" >https://isca-speech.org/archive/Interspeech_2020/pdfs/2173.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21437/Interspeech.2020-2173" target="_blank" >10.21437/Interspeech.2020-2173</a>
Alternative languages
Result language
angličtina
Original language name
Automatic Speech Recognition Benchmark for Air-Traffic Communications
Original language description
Advances in Automatic Speech Recognition (ASR) over the last decade opened new areas of speech-based automation such as in Air-Traffic Control (ATC) environments. Currently, voice communication and data links communications are the only way of contact between pilots and Air-Traffic Controllers (ATCo), where the former is the most widely used and the latter is a non-spoken method mandatory for oceanic messages and limited for some domestic issues. ASR systems on ATCo environments inherit increasing complexity due to accents from non- English speakers, cockpit noise, speaker-dependent biases and small in-domain ATC databases for training. Hereby, we introduce CleanSky EC-H2020 ATCO2, a project that aims to develop an ASR-based platform to collect, organize and automatically pre-process ATCo speech-data from air space. This paper conveys an exploratory benchmark of several state-ofthe- art ASR models trained on more than 170 hours of ATCo speech-data. We demonstrate that the cross-accent flaws due to speakers accents are minimized due to the amount of data, making the system feasible for ATC environments. The developed ASR system achieves an averaged word error rate (WER) of 7.75% across four databases. An additional 35% relative improvement in WER is achieved on one test set when training a TDNNF system with byte-pair encoding.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of Interspeech 2020
ISBN
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ISSN
1990-9772
e-ISSN
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Number of pages
5
Pages from-to
2297-2301
Publisher name
International Speech Communication Association
Place of publication
Shanghai
Event location
Sanghai
Event date
Oct 25, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000833594102086