A confidence measure invariant to language and grammar

Confidence measures are necessary in all voiced activated applications to decide whether a recognized word, or a sentence, should be accepted or rejected. A confidence measure should not only be reliable, but possibly application independent, i.e. its dynamic range should be uniform for different languages, grammars, and vocabularies. This is an important practical issue because it allows the application developers to use the same value of the threshold for different applications and to expect comparable rejection rates. This eases their task at least in the first phase of application development. In this paper, we introduce a confidence measure that has these properties. It allows eliminating the cumbersome experimental procedure necessary to tune individually the rejection threshold for every developed recognition object. We present the results of a set of experiments that demonstrate the “normalization” quality of our confidence measure for six different grammars in different languages.