A speech interface for air traffic control terminals

Ferreiros, Javier; Pardo, José Manuel; Córdoba, Ricardo de; Macías-Guarasa, Javier; Montero, Juan Manuel; Fernández, Francisco García; Sama, Valentín; D’Haro, Luis Fernando; González, Gabriel Villarrubia

doi:10.1016/j.ast.2011.05.002

Cited by 15 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Normally, between eight and ten man-hours of effort [9] (mainly because it requires highly trained participants, often active or retired ATCos) are required to annotate one hour of raw ATCo-pilot voice communication. Afterwards, nearly ten to fifteen minutes of ATCos voice activity are obtained after silence removal; hence, approximately one man-week of work is required to get an hour of ATCos without silence [9,10]. Therefore, it is of great interest to develop a robust speech-to-text and speech-to-call sign system that is aided by context information (air-surveillance data), capable of recognize spoken call signs independently of the accent, airport, or ATCo's origin.…”

Section: Motivationmentioning

confidence: 99%

Automatic Call Sign Detection: Matching Air Surveillance Data with Air Traffic Spoken Communications

Zuluaga-Gómez

Veselý

Blatt

et al. 2020

8th OpenSky Symposium 2020

View full text Add to dashboard Cite

Voice communication is the main channel to exchange information between pilots and Air-Traffic Controllers (ATCos). Recently, several projects have explored the employment of speech recognition technology to automatically extract spoken key information such as call signs, commands, and values, which can be used to reduce ATCos’ workload and increase performance and safety in Air-Traffic Control (ATC)-related activities. Nevertheless, the collection of ATC speech data is very demanding, expensive, and limited to the intrinsic speakers’ characteristics. As a solution, this paper presents ATCO2, a project that aims to develop a unique platform to collect, organize, and pre-process ATC data collected from air space. Initially, the data are gathered directly through publicly accessible radio frequency channels with VHF receivers and LiveATC, which can be considered as an “unlimited-source” of low-quality data. The ATCO2 project explores employing context information such as radar and air surveillance data (collected with ADS-B and Mode S) from the OpenSky Network (OSN) to correlate call signs automatically extracted from voice communication with those available from ADS-B channels, to eventually increase the overall call sign detection rates. More specifically, the timestamp and location of the spoken command (issued by the ATCo by voice) are extracted, and a query is sent to the OSN server to retrieve the call sign tags in ICAO format for the airplanes corresponding to the given area. Then, a word sequence provided by an automatic speech recognition system is fed into a Natural Language Processing (NLP) based module together with the set of call signs available from the ADS-B channels. The NLP module extracts the call sign, command, and command arguments from the spoken utterance.

show abstract

Section: Motivationmentioning

confidence: 99%

Automatic Call Sign Detection: Matching Air Surveillance Data with Air Traffic Spoken Communications

Zuluaga-Gómez

Veselý

Blatt

et al. 2020

8th OpenSky Symposium 2020

View full text Add to dashboard Cite

show abstract

“…Here classical parameter values σ = 10, β = 8/3, each fixed point C± has a pair of complex eigenvalues with positive real part, and one real, negative eigenvalue. The origin is an odd point with two negative and one positive eigenvalue [36,37,38] satisfying 0 < − λ3 < λ1 < − λ2.…”

Section: A Ynamic Systemsmentioning

confidence: 99%

Design of a Web Interface for Fractional Chaotic Systems

Kaçar

Akgül²,

Erguzel³

et al. 2014

IJCNIS

View full text Add to dashboard Cite

There exists a great number of work related to chaotic systems investigated by many researchers, especially about Lorenz chaotic system. If the order of differentiation of variables are fractional, the systems are called fractional chaotic systems. In this work a webbased interface is designed for fractional composition of five different chaotic systems. The interface takes initial and fractional differentiation values and yields output signals and phase portraits. The paper first introduces design tools and then provides results obtained throughout the experiments.

show abstract

“…Thus, the ASR system becomes a promising technique to bridge the speech communication and ATC system. Recently, more and more attention has been paid to employ the ASR techniques to empower ATC applications, such as the ATC assistance system [1], operational safety monitoring system [2,3], and the ATCO training system [4,5].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

A speech interface for air traffic control terminals

Cited by 15 publications

References 15 publications

Automatic Call Sign Detection: Matching Air Surveillance Data with Air Traffic Spoken Communications

Automatic Call Sign Detection: Matching Air Surveillance Data with Air Traffic Spoken Communications

Design of a Web Interface for Fractional Chaotic Systems

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

Contact Info

Product

Resources

About