The use of prior situational/contextual knowledge about a given task can significantly improve Automatic Speech Recognition (ASR) performance. This is typically done through adaptation of acoustic or language models if data is available, or using knowledge-based rescoring. The main adaptation techniques, however, are either domain-specific, which makes them inadequate for other tasks, or static and offline, and therefore cannot deal with dynamic knowledge. To circumvent this problem, we propose a real-time system which dynamically integrates situational context into ASR.
The context integration is done either post-recognition, in which case a weighted Levenshtein distance between the ASR hypotheses and the context information, based on the ASR confidence scores, is proposed to extract the most likely sequence of spoken words, or pre-recognition, where the search space is adjusted to the new situational knowledge through adaptation of the finite state machine modeling the spoken language. Experiments conducted on 3 hours of Air Traffic Control (ATC) data achieved a reduction of the Command Error Rate (CmdER), which is used as evaluation metric in the ATC domain, by a factor of 4 compared to using no contextual knowledge.