Use of acoustic prior information for confidence measure in ASR applications

In this paper, we propose a new acoustic confidence measure of ASR hypothesis and compare it to approaches proposed in the literature. This approach takes into account prior information on the acoustic model performance specific to each phoneme. The new method is tested on two types of recognition errors: the out-of-vocabulary words and the errors due to additive noise. We then propose an efficient way to interpret the raw confidence measure as a correctness prior probability.