Algorithms for approximate string matching
Information and Control
Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Efficient multilingual phoneme-to-grapheme conversion based on HMM
Computational Linguistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
An Extension of the String-to-String Correction Problem
Journal of the ACM (JACM)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Approach to Learning Costs for Graph Edit Distance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Edit-distance of weighted automata
CIAA'02 Proceedings of the 7th international conference on Implementation and application of automata
On triangulating dynamic graphical models
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Cross-domain matching for automatic tag extraction across redundant handwriting and speech events
Proceedings of the 2007 workshop on Tagging, mining and retrieval of human related activity information
Evaluation of several phonetic similarity algorithms on the task of cognate identification
LD '06 Proceedings of the Workshop on Linguistic Distances
Semantic and phonetic automatic reconstruction of medical dictations
Computer Speech and Language
Discriminative pronunciation modeling: a large-margin, feature-rich approach
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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Sitting at the intersection between statistics and machine learning, Dynamic Bayesian Networks have been applied with much success in many domains, such as speech recognition, vision, and computational biology. While Natural Language Processing increasingly relies on statistical methods, we think they have yet to use Graphical Models to their full potential. In this paper, we report on experiments 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% reduction in error rate compared to a previous transducer-based method of learning edit distance.