Scipediacontent: Scipediacontent moved page Draft Content 925143897 to Kleinert et al 2018a

2021-02-02T02:34:50Z

Scipediacontent moved page Draft Content 925143897 to Kleinert et al 2018a

Scipediacontent: Created page with " == Abstract == utomatic Speech Recognition (ASR) has recently proved to be a useful tool to reduce the workload of air traffic controllers leading to significant gains in op..."

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Created page with " == Abstract == utomatic Speech Recognition (ASR) has recently proved to be a useful tool to reduce the workload of air traffic controllers leading to significant gains in op..."

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== Abstract ==

utomatic Speech Recognition (ASR) has recently proved to be a useful tool to reduce the workload of air traffic controllers leading to significant gains in operational efficiency. Air Traffic Control (ATC) systems in operation rooms around the world generate large amounts of untranscribed speech and radar data each day, which can be utilized to build and improve ASR models. In this paper, we propose an iterative approach that utilizes increasing amounts of untranscribed data to incrementally build the necessary ASR models for an ATC operational area. Our approach uses a semi-supervised learning framework to combine speech and radar data to iteratively update the acoustic model, language model and command prediction model (i.e. prediction of possible commands from radar data for a given air traffic situation) of an ASR system. Starting with seed models built with a limited amount of manually transcribed data, we simulate an operational scenario to adapt and improve the models through semi-supervised learning. Experiments on two independent ATC areas (Vienna and Prague) demonstrate the utility of our proposed methodology that can scale to operational environments with minimal manual effort for learning and adaptation.

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.21437/interspeech.2018-1447 http://dx.doi.org/10.21437/interspeech.2018-1447]

* [https://elib.dlr.de/120924 https://elib.dlr.de/120924]

* [https://publidiap.idiap.ch/downloads//papers/2019/Srinivasamurthy_INTERSPEECH2018_2018.pdf https://publidiap.idiap.ch/downloads//papers/2019/Srinivasamurthy_INTERSPEECH2018_2018.pdf]

* [https://dblp.uni-trier.de/db/conf/interspeech/interspeech2018.html#Srinivasamurthy18 https://dblp.uni-trier.de/db/conf/interspeech/interspeech2018.html#Srinivasamurthy18],
: [https://academic.microsoft.com/#/detail/2888842383 https://academic.microsoft.com/#/detail/2888842383]

* [https://publidiap.idiap.ch/downloads//papers/2019/Srinivasamurthy_INTERSPEECH2018_2018.pdf https://publidiap.idiap.ch/downloads//papers/2019/Srinivasamurthy_INTERSPEECH2018_2018.pdf],
: [http://infoscience.epfl.ch/record/263106 http://infoscience.epfl.ch/record/263106]

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Scipediacontent: Scipediacontent moved page Draft Content 925143897 to Kleinert et al 2018a

Scipediacontent: Created page with " == Abstract == utomatic Speech Recognition (ASR) has recently proved to be a useful tool to reduce the workload of air traffic controllers leading to significant gains in op..."