A New Approach to Utterance Verification Based on Neighborhood Information in Model Space

In this paper, we propose to use neighborhood information in model space to perform utterance verification (UV). At first, we present a nested-neighborhood structure for each underlying model in model space and assume the underlying model's competing models sit in one of these neighborhoods, which is used to model alternative hypothesis in UV. Bayes factors (BF) is first introduced to UV and used as a major tool to calculate confidence measures based on the above idea. Experimental results in Bell Labs communicator system show that the new method has dramatically improved verification performance when verifying correct words against mis-recognized words in recognizer's output, relatively more than 20% reduction in equal error rate (EER) when comparing with the standard approach based on likelihood ratio testing and anti-models.