Using output probability distribution for oov word rejection

Abstract

This paper proposes a method to calculate the confidence score for out-of-vocabulary (OOV) word verification based on the Output Probability Distribution (OPD) of phoneme HMMs. Compared with input vector for dynamic garbage model, OPD vector contains more information than the sorted probabilities. Confidence score of each phoneme is calculated by SVM with OPD vectors as input. Hypotheses are accepted or rejected based on this confidence score. Experimental results showed that the proposed method achieved lower EER in word verification task than the conventional dynamic garbage model.