Garbage modeling with decoys for a sequential recognition scenario

This paper is concerned with a speech recognition scenario where two unequal ASR systems, one fast with constrained resources, the other significantly slower but also much more powerful, work together in a sequential manner. In particular, we focus on decisions when to accept the results of the first recognizer and when the second recognizer needs to be consulted. As a kind of application-dependent garbage modeling, we suggest an algorithm that augments the grammar of the first recognizer with those valid paths through the language model of the second recognizer that are confusable with the phrases from this grammar. We show how this algorithm outperforms a system that only looks at recognition confidences by about 20% relative.