Discriminative recognition rate estimation for N-best list and its application to N-best rescoring

Techniques for estimating recognition rates without using reference transcriptions are essential if we are to judge whether or not speech recognition technology is applicable to a new task. We have proposed a discriminative recognition rate estimation (DRRE) method for 1-best recognition hypotheses and shown its good estimation performance experimentally. In this paper, we extend our DRRE to N-best lists of recognition hypotheses by modifying its feature extraction procedures and efficiently selecting N-best hypotheses for its discriminative model training. In addition, we apply our extended DRRE to N-best rescoring. In the experiments, the extended DRRE also showed good estimation performance for the N-best lists. And using the estimated recognition rates, the 1-best word accuracy was significantly improved by N-best rescoring from the baseline.