Automatic confidence score mapping for adapted speech recognition systems

In practical automatic speech recognition (ASR) systems, the effects of modeling improvements on both in-grammar (IG) and out-of-grammar (OOG) errors are important. While adapted models are known to decrease IG error, they may increase OOG error. This is because adapted models tend to produce higher confidence scores, resulting in fewer OOG utterances being rejected at the same confidence-score threshold. In this paper, we present an a1gorithm to map confidence scores, so that model adaptation gives reduced IG error with no degradation in OOG error. Experimental results are presented in the context of unsupervised task adaptation.