High-accuracy phone recognition by combining high-performance lattice generation and knowledge based rescoring

Abstract

This study is a result of a collaboration project between two groups, one from Brno University of Technology and the other from Georgia Institute of Technology (GT). Recently the Brno recognizer is known to outperform many state-of-the-art systems on phone recognition, while the GT knowledge-based lattice rescoring module has been shown to improve system performance on a number of speech recognition tasks. We believe a combination of the two system results in high-accuracy phone recognition. To integrate the two very different modules, we modify Brno's phone recognizer into a phone lattice hypothesizer to produce high-quality phone lattices, and feed them directly into the knowledge-based module to rescore the lattices. We test the combined system on the TIMIT continuous phone recognition task without retraining the individual subsystems, and we observe that the phone error rate was effectively reduced to 19.78% from 24.41% produced by the Brno phone recognizer. To the best of the authors' knowledge this result represents the lowest ever error rate reported on the TIMIT continuous phone recognition task.