Contextual adaptation for improving call sign recognition

by admin — last modified Jul 19, 2021 01:47 PM
Contextual adaptation is a technique of “suggesting” small snippets of text that are likely to appear in the speech recognition output. The snippets of text are derived from the current “situation” of the speaker, in our project ATCO this is location and time.

Air Traffic Control Conversations Collection – A legal introduction by ELDA

by Petr Motlicek — last modified Nov 30, 2020 12:00 PM
ELRA, the European Language Resources Association, and its distribution agency, ELDA, have been funded in 1995 and have been a world-wide leading player in distributing Language Resources and providing other services to the speech language communities. In the course of the ATCO2 project, ELDA provides legal expertise for the collection of Air Traffic Control Conversations and Data Management in the project.

Bringing together what belongs together: Matching voice commands and radar data

by Petr Motlicek — last modified Nov 30, 2020 12:01 PM
The whole ATCO² Project relies on two input streams. One input stream is represented by the voice command issued by air-traffic controler and the other input stream is provided by the automatic dependent surveillance-broadcast data. An independent collection of both streams makes a matching of the data inevitable. The following lines will give an insight in how the matching process is done.

Automatic speech recognition, how it works?

by Petr Motlicek — last modified Nov 30, 2020 12:01 PM
[Updated, 3.4.2020]: ATCO2 project is closely aligned with the development of automatic speech recognition engines for Air-Traffic Controllers (ATCOs), particularly to automatically transcribe their communication with the pilots. This blogpost is giving some insight into the process of Automatic Speech Recognition, current trends, and some details on how it will be integrated in the ATCO2 project. We are describing Hybrid HMM-based speech recognizer, which is the current state-of-the-art speech recognition paradigm. The literature also suggests end-to-end systems. However, we did not consider using these, due to practical reasons. We use the toolkit Kaldi [1], both for training the baseline models, and processing the untranscribed data.

The annotation started

by Petr Motlicek — last modified Nov 30, 2020 12:02 PM
[Updated 1. 2. 2020]: The work on ATCO2 project started during Christmas. We kicked-off one of the important phases, which is a data collection and transcription. Why do we need to start with this? Well, this will be explained in larger context below!

Kick-off meeting

by admin — last modified Dec 03, 2019 03:56 PM
The kick-off meeting took place at Brno University of Technology on November 22, 2019.

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