New features based on multiple word graphs for utterance verification

The goal of Utterance Verification is to estimate a confidence measure which helps detecting words in the hypothesized sentence that are likely to have been missrecognized. Word graphs have been extensively employed for directly estimating the confidence measure and for extracting important predictor features. In all the cases, a single word graph which is obtained through the recognition process. In this paper we propose the use of multiple word graphs to compute new features. The experimental study shows that these proposed features outperform those computed on a single word graph and other well-known predictor features. Moreover, the combination of the proposed features along with other kind of features provides improvements in the verification accuracy.