Semi-supervised Adaptation of Assistant Based Speech Recognition Models for different Approach Areas
| Type of publication: | Conference paper |
| Citation: | Kleinert_DASC2018_2018 |
| Publication status: | Published |
| Booktitle: | 37th AIAA/IEEE Digital Avionics Systems Conference |
| Year: | 2018 |
| Month: | September |
| Location: | London |
| Organization: | AIAA/IEEE |
| Note: | The best paper award in cathegory "ST-B: Human Factors & Performance for Aerospace Applications" (http://2018.dasconline.org/pages/award-winners) |
| URL: | http://www. dasconline.org... |
| Abstract: | 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. |
| Keywords: | Assistant Based Speech Recognition, Automatic Speech Recognition, Command Prediction Model, machine learning, unsupervised learning |
| Projects |
Idiap MALORCA |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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