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

Abstract

Air Navigation Service Providers (ANSPs) replace paper flight strips through different digital solutions. The instructed com-mands 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 approaches 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.