Generalized posterior probability for minimizing verification errors at subword, word and sentence levels

Abstract

Generalized posterior probability, a statistical confidence measure, is tested in this study for verifying optimally the recognized units at the subword, word and sentence levels. We developed the generalized posterior probability by analyzing the exponential weights of the acoustic and language model scores to minimize the total verification errors at different unit levels. Experimental results have demonstrated the effectiveness of this generalized confidence measure for verifying Chinese LVCSR output. The Chinese Basic Travel Expression Corpus (BTEC) is used for evaluation and the relative improvement of confidence error rate (CER) over the baseline performance is 47.76% for sentences, 27.31% for words and 4.64% for subwords.