53 research outputs found
Validating Automatic Speech Recognition and Understanding for Pre-Filling Radar Labels - Increasing Safety While Reducing Air Traffic Controllers' Workload
Customization of Automatic Speech Recognition Engines for Rare Word Detection Without Costly Model Re-Training
Thanks to Alexa, Siri or Google Assistant automatic speech recognition (ASR) has changed our daily life during the last decade. Prototypic applications in the air traffic management (ATM) domain are available. Recently pre-filling radar label entries by ASR support has reached the technology readiness level before industrialization (TRL6). However, seldom spoken and airspace related words relevant in the ATM context remain a challenge for sophisticated applications. Open-source ASR toolkits or large pre-trained models for experts - allowing to tailor ASR to new domains - can be exploited with a typical constraint on availability of certain amount of domain specific training data, i.e., typically transcribed speech for adapting acoustic and/or language models. In general, it is sufficient for a "universal" ASR engine to reliably recognize a few hundred words that form the vocabulary of the voice communications between air traffic controllers and pilots. However, for each airport some hundred dependent words that are seldom spoken need to be integrated. These challenging word entities comprise special airline designators and waypoint names like "dexon" or "burok", which only appear in a specific region. When used, they are highly informative and thus require high recognition accuracies. Allowing plug and play customization with a minimum expert manipulation assumes that no additional training is required, i.e., fine-tuning the universal ASR. This paper presents an innovative approach to automatically integrate new specific word entities to the universal ASR system. The recognition rate of these region-specific word entities with respect to the universal ASR increases by a factor of 6
Safety Aspects of Supporting Apron Controllers with Automatic Speech Recognition and Understanding Integrated into an Advanced Surface Movement Guidance and Control System
The information air traffic controllers (ATCos) communicate via radio telephony is valuable for digital assistants to provide additional safety. Yet, ATCos have to enter this information manually. Assistant-based speech recognition (ABSR) has proven to be a lightweight technology that automatically extracts and successfully feeds the content of ATC communication into digital systems without additional human effort. This article explains how ABSR can be integrated into an advanced surface movement guidance and control system (A-SMGCS). The described validations were performed in the complex apron simulation training environment of Frankfurt Airport with 14 apron controllers in a human-in-the-loop simulation in summer 2022. The integration significantly reduces the workload of controllers and increases safety as well as overall performance. Based on a word error rate of 3.1%, the command recognition rate was 91.8% with a callsign recognition rate of 97.4%. This performance was enabled by the integration of A-SMGCS and ABSR: the command recognition rate improves by more than 15% absolute by considering A-SMGCS data in ABSR
Sprachverarbeitung im 4D-CARMA System -Bedienungsanleitung-
Dieses Dokument beschreibt den Startvorgang des Planungssystems 4D-CARMA und allen zugehörigen Komponenten. Für den eiligen Leser ist in Kapitel 4 eine Kurzbeschreibung ent-halte
4D-CARMA - Bedienungsanleitung
Dieses Dokument beschreibt den Startvorgang des Planungssystems 4D-CARMA und allen zugehörigen Komponenten. Für den eiligen Leser ist in Kapitel 4 Kurzbeschreibung enthalten.
Das 4D-CARMA System wurde für den Flughafen Wien entwickelt. Die Anbindung an die realen Systeme auf dem Flughafen Wien ist noch nicht abgeschlossen (wurde noch nicht beauftragt). Das hier beschriebene System wird als Stand alone Variante mit aufgezeichneten Daten aus einer Datei gespeist
4D-CARMA - INDRA Socketinterface
For the presentation of the integration from DLRs 4D-CARMA, including a speech recognizer,
into INDRAs simulation it is necessary to make a data exchange. 4D-CARMA needs input
data like radar and time information to optimize the traffic. As output speech data is provided.
This document describes the data exchange between INDRAs simulation and DLRs 4DCARMA
Aviator II Interface AviaSim 4D-CARMA
This document describes the input and output data that are provided via PVM by 4DCARM
World ATM Congress 2014 AcListant Bedienungsanleitung
Auf dem World ATM Congress 2014 in Madrid wird als Exponat AcListant gezeigt. Dieses Dokument beschreibt den Messeaufbau sowie die Start – und Stopp Prozeduren
Anflug-, Rollverkehrs- und Abflug-Management 4D-CARMA - Integrationstest - Konzept
Software is soft. Changes are the rule. Early detection of errors due to software maintenance
is important for software quality. Otherwise no estimates for the implantation of new functionality
are possible.
For optimization and compliance with the required quality standards and to assess the impact
of software changes on the overall system extensive and time-consuming tests could be
a necessary part of the software development process. Also a high number of personnel is
required. This burden is reduced with every day automatically running tests.
In this report we therefore describe daily automatic integration tests to assure software quality
of the arrival manager 4D-CARMA
Anflug-, Rollverkehrs- und Abflug-Management 4D-CARMA - 3D Display -Socketinterface
Der Radarbildschirm ist die für den Fluglotsen das Arbeitsmedium zur Darstellung der aktuel-len Luftlage. Die heutigen eingesetzten Anzeigen beruhen alle auf einer 2 Dimensionalen Darstellung des Luftraums. Zukünftig könnte eine 3-dimensionale Darstellung den Lotsenar-beitsplatz verändern. Zu diesem Zweck wurde vom DFKI ein Prototyp eines 3-dimensionalen Radarbildschirms entwickelt, welches mit Daten vom AMAN des DLR versorgt werden soll.
Diese Schnittstelle wurde als Socketverbindung realisiert.
In einer ersten Version liefert die Schnittstelle die Daten aus einer Datei über eine tcp/ip So-cket Verbindung an das Display. Für die weitere Entwicklung ist die Anbindung der Socket Schnittstelle an die AMAN Datenbank vorgesehen sowie die unabhängige Versorgung meh-rer Displays.
Dieses Dokument beschreibt den Aufbau der Schnittstelle, sowie die Formate der übertrage-nen Daten.
In Kapitel 2 wird der schematische Aufbau der Schnittstelle beschrieben. Kapitel 3 und 4 beschreiben die einzelnen Programmteile und in Kapitel 5 werden die Datenformate be-schrieben.
Der Programmteil aus Kapitel 4 kann vom DFKI als Datenempfänger benutzt werden
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