Vienne (Autriche)

Résumé

thumb|Sceau. alt=|vignette|Logo. Vienne ( ; Wien ; Wean ; Bécs ; Beč ; Dunaj) est la capitale et la plus grande ville de l'Autriche ; elle est aussi l'un des neuf Länder (État fédéré) du pays (Bundesland Wien). La ville est située dans l'est du pays et traversée par le Danube (Donau). Capitale du duché puis archiduché d'Autriche, elle fut de fait celle du monde germanique durant le règne de la maison de Habsbourg (devenue en 1745 la maison de Habsbourg-Lorraine) sur le Saint-Empire romain germanique (1273 – 1291, 1298 – 1308, 1438 – 1806) puis présida la Confédération germanique (1815 – 1866). Elle fut en même temps celle de l'empire d'Autriche (1804 – 1867) puis de l'Autriche-Hongrie (1867 – 1918). Elle a été le point de départ de la crise bancaire de mai 1873. Depuis plusieurs années, elle figure dans les premières places de nombreux classements internationaux récompensant la qualité de vie des métropoles. Par exemple, la ville est en tête du classement selon l'indice Mercer évalu

Source officielle

https://fr.wikipedia.org/wiki/Vienne_(Autriche)

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Building Blocks of Assistant Based Speech Recognition for Air Traffic Management Applications

Petr Motlicek, Mittul Singh

In air traffic control rooms around the world paper flight strips are replaced through different digital solutions. This enables other systems to access the instructed air traffic controller (ATCo) commands and use them for other purposes. Digital flight strip solutions, however, require manual input from the ATCo and, therefore, increase the workload. Recently the AcListant® project has validated that Assistant Based Speech Recognition (ABSR, which integrates a speech recognizer with an assistant system) could be a solution to avoid this increase of workload. However, adaptation of ABSR to new environments usually requires a lot of data, time and expertise, which makes the process expensive. The MALORCA project used machine learning (ML) algorithms to provide a generic, cheap and effective approach for adaptation. Therefore, ABSR was divided into conceptual modules that contain generic parts (building blocks) and domain specific models. As first show case ABSR was auto-matically adapted with radar data and voice recordings from Prague and Vienna approach. The fully trained system reaches command recognition rates (RR) of 92% (Prague) resp. 83% (Vienna) and command recognition error rates (ER) of 0.6% (Prague) resp. 3.2% (Vienna). The building blocks and models and their effect on RR and ER are presented in this paper.

SESARJU2018

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

Petr Motlicek, Mittul Singh

Air Navigation Service Provider (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 target-domain 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 approach 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.

2018

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A Wealth of Typological Solutions from the Twenties: Vienna and Frankfurt

Alessandro Porotto

2019

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