Generalized posterior probability for minimum error verification of recognized sentences

Generalized posterior probability (GPP) is investigated in this paper as a statistical confidence measure for verifying recognized sentences of a large vocabulary continuous speech recognition system (LVCSR). We optimize the GPP by training the exponential weights of the acoustic and language models and decision threshold to minimize total verification errors. Two utterance level confidence measures: generalized utterance posterior probability (GUPP) and product of generalized word posterior probabilities (GWPP) of component words in a string hypothesis are tested. When evaluated on the Chinese Basic Travel Expression Corpus (BTEC), 47.9% and 53.9% relative improvement of utterance confidence error rate (CER) have been obtained for the GUPP and product of GWPP confidence measures, respectively.