Context-based recognition network adaptation for improving on-line ASR in Air Traffic Control


This paper presents an approach for incorporating situational context information into an on-line Automatic Speech Recognition (ASR) component of an Air Traffic Control (ATC) assistance system to improve recognition performance. Context information is treated as prior information to reduce the search space for recognition. It is integrated in the ASR pipeline by continually updating the recognition network. This is achieved by automatically adapting the underlying grammar whenever new situational knowledge becomes available. The context-dependent recognition network is then re-created and substituted for recognition based on these context-dependent grammars. As a result, the recognizer’s search space is constantly being limited to that subset of hypotheses that are deemed plausible in the current situation. Since recognition and adaptation tasks can be easily performed by two separate parallel processes, on-line capabilities of the system are maintained, and response times do not increase as a result of context integration. Experiments conducted on about two hours of ATC data show a reduction in command error rate by a factor of three when context is used.

In Proceedings of the Spoken Language Technology Workshop (SLT 2014), IEEE.