For many practical applications of speech recognition systems, it is desirable to have an estimate of con dence for each hypothesized word, i.e. to have an estimate which words of the speech recognizer's output are likely to be correct and which are not reliable. Many oftoday's speech recognition systems use word lattices as a compact representation of a set of alternative hypothesis. We exploit the use of such word lattices as information sources for the measure-of-con dence tagger JANKA [1]. In experiments on spontaneous human-to-human speech data the use of word lattice related information signi cantly improves the tagging accuracy.

For many practical applications of speech recognition systems, it is desirable to have an estimate of confidence for each hypothesized word, i.e. to have an estimate of which words of the output of the speech recognizer are likely to be correct and which are not reliable. We describe the development of the measure of confidence tagger JANKA, which is able to provide confidence information for the words in the output of the speech recognizer JANUS-3-SR. On a spontaneous german human-to-human database, JANKA achieves a tagging accuracy of 90% at a baseline word accuracy of 82%. 1. INTRODUCTION Current speech recognition systems are far from perfect. Unfortunately, number and location of the errors in their output is usually unknown. This information, however, could be used in a number of applications. Examples for such applications are word selection for unsupervised adaptation schemes like MLLR [1], automatic weighting of additional, non-speech knowledge sources like lip-reading, or ai...