Reducing controller workload with automatic speech recognition

Helmke, Hartmut; Ohneiser, Oliver; Mühlhausen, Thorsten; Wies, Matthias

doi:10.1109/dasc.2016.7778024

Cited by 35 publications

(23 citation statements)

References 5 publications

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“…Multimodal HMIs combine different interaction modalities, aiming to support a natural [1] and efficient way of human communication [2,3]. Recent research has revealed that reasonable interaction technologies [4] for a CWP should recognize touch, speech, and gaze [5][6][7][8]. In accordance with these findings, the German Aerospace Center (DLR) has developed the multimodal CWP TriControl concept, which combines automatic speech recognition, multitouch gestures with one or multiple fingers on a touch input device, and eye-tracking via infrared sensors located at the bottom of the monitor.…”

Section: Of 26mentioning

confidence: 99%

Operational Feasibility Analysis of the Multimodal Controller Working Position “TriControl”

et al. 2020

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Current Air Traffic Controller working positions (CWPs) are reaching their capacity owing to increasing levels of air traffic. The multimodal CWP prototype TriControl combines automatic speech recognition, multitouch gestures, and eye-tracking, aiming for more natural and improved human interaction with air traffic control systems. However, the prototype has not yet undergone systematic evaluation with respect to feasibility. This paper evaluates the operational feasibility, focusing on the system usability of the approach CWP TriControl and its fulfillment of operational requirements. Fourteen controllers took part in a simulation study to evaluate the TriControl concept. The active approach controllers among the group of participants served as the main core target subgroup. The ratings of all controllers in the TriControl assessment were, on average, generally in slight agreement, with just a few showing statistical significance. However, the active approach controllers performed better and rated the system much more positively. The active approach controllers were strongly positive regarding the system usability and acceptance of this early-stage prototype. Particularly, ease of use, user-friendliness, and learnability were perceived very positively. Overall, they were also satisfied with the command input procedure, and would use it for their daily work. Thus, the participating controllers encourage further enhancements to be made to TriControl.

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Section: Of 26mentioning

confidence: 99%

Operational Feasibility Analysis of the Multimodal Controller Working Position “TriControl”

et al. 2020

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“…Input is predominantly provided manually by mouse, as well as with keyboard inputs. As shown in [30] ASR could be a solution to reduce controllers' workload significantly. The input needed for the radar labels could be directly extracted from the controller-pilot communication.…”

Section: Other Benefitting Applicationsmentioning

confidence: 99%

“…DLR and Saarland University have shown in the AcListant® project that command recognition rates of 95% with command recognition error rates below 2% are possible [29]. Its follow-up project, AcListant®-Strips validated that Assistant Based Speech Recognition (ABSR) can reduce controllers' workload for radar label maintenance by a factor of three [30] and that fuel savings of 60 liters of kerosene per flight are possible [11]. In this exercise Air Navigation Services of Czech Republic (ANS CR), DLR, the aviation consultant Integra and Thales ATM Group concentrated on the safety aspects of ASR when used as input device for radar label maintenance instead of a mouse.…”

Section: B Safety Assessment Of Asr For Radar Label Maintenancementioning

confidence: 99%

Ontology for Transcription of ATC Speech Commands of SESAR 2020 Solution PJ.16-04

Helmke

Slotty²,

Poiger

et al. 2018

2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC)

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Nowadays Automatic Speech Recognition (ASR) applications are increasingly successful in the air traffic (ATC) domain. Paramount to achieving this is collecting enough data for speech recognition model training. Thousands of hours of ATC communication are recorded every day. However, the transcription of these data sets is resource intense, i.e. writing down the sequence of spoken words, and more importantly, interpreting the relevant semantics. Many different approaches including CPDLC (Controller Pilot Data Link Communications) currently exist in the ATC community for command transcription, a fact that e.g. complicates exchange of transcriptions. The partners of the SESAR funded solution PJ.16-04 are currently developing on a common ontology for transcription of controller-pilot communications, which will harmonize integration of ASR into controller working positions. The resulting ontology is presented in this paper.

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“…The ABSR system developed by Saarland University (USAAR) and DLR analyses the controller pilot communication and shows the recognized commands in the radar label directly to the ATCo [2]. As command recognition rates better than 95% were achieved for Dusseldorf approach area, the controller only needs to manually correct the output of the speech recognizer in less than one of twenty cases [3]. The controller gets additional free cognitive resources, which increase safety.…”

Section: A Problemmentioning

confidence: 99%

Semi-supervised Adaptation of Assistant Based Speech Recognition Models for different Approach Areas

Kleinert

Helmke

Siol

et al. 2018

2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC)

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Air Navigation Service Providers (ANSPs) replace paper flight strips through different digital solutions. The instructed commands from an air traffic controller (ATCos) are then available in computer readable form. However, those systems require manual controller inputs, i.e. ATCos' workload increases. The Active Listening Assistant (AcListant®) project has shown that Assistant Based Speech Recognition (ABSR) is a potential solution to reduce this additional workload. However, the development of an ABSR application for a specific targetdomain usually requires a large amount of manually transcribed audio data in order to achieve task-sufficient recognition accuracies. MALORCA project developed an initial basic ABSR system and semi-automatically tailored its recognition models for both Prague and Vienna approaches by machine learning from automatically transcribed audio data. Command recognition error rates were reduced from 7.9% to under 0.6% for Prague and from 18.9% to 3.2% for Vienna.

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Reducing controller workload with automatic speech recognition

Cited by 35 publications

References 5 publications

Operational Feasibility Analysis of the Multimodal Controller Working Position “TriControl”

Operational Feasibility Analysis of the Multimodal Controller Working Position “TriControl”

Ontology for Transcription of ATC Speech Commands of SESAR 2020 Solution PJ.16-04

Semi-supervised Adaptation of Assistant Based Speech Recognition Models for different Approach Areas

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