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 51% reduction of
the Command Error Rate (CmdER) which is used as evaluation
metric in the ATC domain
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