Robust confidence annotation and rejection for continuous speech recognition

We are looking for confidence scoring techniques that perform well on a broad variety of tasks. Our main focus is on word-level error rejection, but most results apply to other scenarios as well. A variation of the normalized cross entropy that is adapted to that purpose is introduced. It is successfully used to automatically select features and optimize the word-level confidence measure on several test sets. Sentence-level confidence geared toward the rejection of out-of-grammar utterances is also investigated. The combination of a word graph based technique and the acoustic score shows excellent performance across all the tasks we considered.