A Context-Aware Language Model to Improve the Speech Recognition in Air Traffic Control

Recognizing isolated digits of the flight callsign is an important and challenging task for automatic speech recognition (ASR) in air traffic control (ATC). Fortunately, the flight callsign is a kind of prior ATC knowledge and is available from dynamic contextual information. In this work, we attemp...

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Autores principales:	Dongyue Guo, Zichen Zhang, Peng Fan, Jianwei Zhang, Bo Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	language model automatic speech recognition air traffic control dynamic contextual information Motor vehicles. Aeronautics. Astronautics TL1-4050
Acceso en línea:	https://doaj.org/article/d5c3f6acd8874e9bb51a0fe02f3ccc33
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spelling	oai:doaj.org-article:d5c3f6acd8874e9bb51a0fe02f3ccc332021-11-25T15:57:47ZA Context-Aware Language Model to Improve the Speech Recognition in Air Traffic Control10.3390/aerospace81103482226-4310https://doaj.org/article/d5c3f6acd8874e9bb51a0fe02f3ccc332021-11-01T00:00:00Zhttps://www.mdpi.com/2226-4310/8/11/348https://doaj.org/toc/2226-4310Recognizing isolated digits of the flight callsign is an important and challenging task for automatic speech recognition (ASR) in air traffic control (ATC). Fortunately, the flight callsign is a kind of prior ATC knowledge and is available from dynamic contextual information. In this work, we attempt to utilize this prior knowledge to improve the performance of the callsign identification by integrating it into the language model (LM). The proposed approach is named context-aware language model (CALM), which can be applied for both the ASR decoding and rescoring phase. The proposed model is implemented with an encoder–decoder architecture, in which an extra context encoder is proposed to consider the contextual information. A shared embedding layer is designed to capture the correlations between the ASR text and contextual information. The context attention is introduced to learn discriminative representations to support the decoder module. Finally, the proposed approach is validated with an end-to-end ASR model on a multilingual real-world corpus (ATCSpeech). Experimental results demonstrate that the proposed CALM outperforms other baselines for both the ASR and callsign identification task, and can be practically migrated to a real-time environment.Dongyue GuoZichen ZhangPeng FanJianwei ZhangBo YangMDPI AGarticlelanguage modelautomatic speech recognitionair traffic controldynamic contextual informationMotor vehicles. Aeronautics. AstronauticsTL1-4050ENAerospace, Vol 8, Iss 348, p 348 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	language model automatic speech recognition air traffic control dynamic contextual information Motor vehicles. Aeronautics. Astronautics TL1-4050
spellingShingle	language model automatic speech recognition air traffic control dynamic contextual information Motor vehicles. Aeronautics. Astronautics TL1-4050 Dongyue Guo Zichen Zhang Peng Fan Jianwei Zhang Bo Yang A Context-Aware Language Model to Improve the Speech Recognition in Air Traffic Control
description	Recognizing isolated digits of the flight callsign is an important and challenging task for automatic speech recognition (ASR) in air traffic control (ATC). Fortunately, the flight callsign is a kind of prior ATC knowledge and is available from dynamic contextual information. In this work, we attempt to utilize this prior knowledge to improve the performance of the callsign identification by integrating it into the language model (LM). The proposed approach is named context-aware language model (CALM), which can be applied for both the ASR decoding and rescoring phase. The proposed model is implemented with an encoder–decoder architecture, in which an extra context encoder is proposed to consider the contextual information. A shared embedding layer is designed to capture the correlations between the ASR text and contextual information. The context attention is introduced to learn discriminative representations to support the decoder module. Finally, the proposed approach is validated with an end-to-end ASR model on a multilingual real-world corpus (ATCSpeech). Experimental results demonstrate that the proposed CALM outperforms other baselines for both the ASR and callsign identification task, and can be practically migrated to a real-time environment.
format	article
author	Dongyue Guo Zichen Zhang Peng Fan Jianwei Zhang Bo Yang
author_facet	Dongyue Guo Zichen Zhang Peng Fan Jianwei Zhang Bo Yang
author_sort	Dongyue Guo
title	A Context-Aware Language Model to Improve the Speech Recognition in Air Traffic Control
title_short	A Context-Aware Language Model to Improve the Speech Recognition in Air Traffic Control
title_full	A Context-Aware Language Model to Improve the Speech Recognition in Air Traffic Control
title_fullStr	A Context-Aware Language Model to Improve the Speech Recognition in Air Traffic Control
title_full_unstemmed	A Context-Aware Language Model to Improve the Speech Recognition in Air Traffic Control
title_sort	context-aware language model to improve the speech recognition in air traffic control
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d5c3f6acd8874e9bb51a0fe02f3ccc33
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A Context-Aware Language Model to Improve the Speech Recognition in Air Traffic Control

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