Speaker Adaptive Confidence Scoring Using Bayesian Combining
Taeyoon Kim, Hanseok Ko · 2006
Bayesian combining of confidence measures is proposed for speech recognition. Bayesian combining is achieved by the estimation of joint pdf of confidence feature vector in correct and incorrect hypothesis classes. If the joint pdf in the two classes are correctly estimated, this method guarantees an optimal combining in the minimum Bayes risk sense. Investigating the distribution of confidence features, we found out that the pdf are well estimated by the Gaussian mixture model with full covariance matrix in combining small number of features. In addition, the adaptation of a confidence score by adapting the joint pdf is presented. The proposed methods reduced the classification error rate by 17% from the conventional single feature based confidence scoring method in an isolated word out-of-vocabulary rejection test.