Paper: A Dynamic Bayesian Framework To Model Context And Memory In Edit Distance Learning: An Application To Pronunciation Classification

ACL ID P05-1042
Title A Dynamic Bayesian Framework To Model Context And Memory In Edit Distance Learning: An Application To Pronunciation Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

Sitting at the intersection between statis- tics and machine learning, Dynamic Bayesian Networks have been applied with much success in many domains, such as speech recognition, vision, and compu- tational biology. While Natural Language Processing increasingly relies on statisti- cal methods, we think they have yet to use Graphical Models to their full poten- tial. In this paper, we report on experi- ments in learning edit distance costs using Dynamic Bayesian Networks and present results on a pronunciation classification task. By exploiting the ability within the DBN framework to rapidly explore a large model space, we obtain a 40% reduc- tion in error rate compared to a previous transducer-based method of learning edit distance.