Word graph rescoring using confidence measures

This paper presents a novel approach to using confidence scores for word graph rescoring. For each word in the system´s vocabulary we computed the probability that the observation is correct given its acoustic score. Afterwards, we used these probabilities for rescoring word graphs outputted by the recognizer. We present some implementation detail as well as accuracy improvements obtained using this method