Utterance verification using improved confidence measures based on alignment confusion rate in Chinese digits recognition

In this paper, we explore an approach to improved confidence measures based on a novel alignment confusion rate (ACR) which integrates alignment information from two different modeling unit sets in Chinese digits recognition system. Both initial-final (IF) phone set and head-body-tail (HBT) models have proven to obtain good recognition performance for connected digit strings. These two different modeling can produce similar results but with different time-marked word boundaries. The objective of our proposed method is combining posterior probability with alignment confusion rate score provided by word alignment of IF-based results to HBT-based reference results that minimizes word error rate to get an effective confidence measure for utterance verification. The efficiency of the proposed algorithm is demonstrated with various experiments on data collected from car-kit microphone.