A context-aware speech recognition and understanding system for air traffic control domain

Abstract

Automatic Speech Recognition and Understanding (ASRU) systems can generally use temporal and situational context information to improve their performance for a given task. This is typically done by rescoring the ASR hypotheses or by dynamically adapting the ASR models. For some domains, such as Air Traffic Control (ATC), this context information can be, however, small in size, partial and available only as abstract concepts (e.g. airline codes), which are difficult to map into full possible spoken sentences to perform rescoring or adaptation. This paper presents a multi-modal ASRU system, which dynamically integrates partial temporal and situational ATC context information to improve its performance. This is done either by 1) extracting word sequences which carry relevant ATC information from ASR N-best Lists and then perform a context-based rescoring on the extracted ATC segments or 2) by a partial adaptation of the language model. Experiments conducted on 4 hours of test data from Prague and Vienna approach (arrivals) showed a relative reduction of the ATC command error rate metric by 30% to 50%.