In this paper, we present several confidence measures for large vocabulary continuous speech recognition. We propose to estimate the confidence of a hypothesized word directly as its posterior probability, given all acoustic observations of the utterance. These probabilities are computed on word graphs using a forward-backward algorithm. We also study the estimation of posterior probabilities on N-best lists instead of word graphs and compare both algorithms in detail. In addition, we compare the posterior probabilities with two alternative confidence measures, i.e., the acoustic stability and the hypothesis density. We present experimental results on five different corpora: the Dutch ARISE lk evaluation corpus, the German Verbmobil '98 7k evaluation corpus, the English North American Business '94 20k and 64k development corpora, and the English Broadcast News '96 65k evaluation corpus. We show that the posterior probabilities computed on word graphs outperform all other confidence measures. The relative reduction in confidence error rate ranges between 19% and 35% compared to the baseline confidence error rate.

In this paper, we introduce a new concept, the time frame error rate. We show that this error rate is closely correlated with the word error rate and use it to overcome the mismatch between Bayes' decision rule which aims at minimizing the expected sentence error rate and the word error rate which is used to assess the performance of speech recognition systems. Based on the time frame errors we derive a new decision rule and show that the word error rate can be reduced consistently with it on various recognition tasks. All stochastic models are left completely unchanged. We present experimental results on five corpora, the Dutch Arise corpus, the German Verbmobil '98 corpus, the English North American Business '94 20k and 64k development corpora, and the English Broadcast News '96 corpus. The relative reduction of the word error rate ranges from 2.3% to 5.1%.