CONF icassp:DimitrakBengio2004/IDIAP Boosting HMMs with an application to speech recognition Dimitrakakis, Christos Bengio, Samy EXTERNAL http://publications.idiap.ch/attachments/papers/2004/dimitrak_bengio03b.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/dimitrakbengio2003a Related documents IEEE International Conference on Acoustic, Speech, and Signal Processing, ICASSP 2004 IDIAP-RR 03-41 Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines its applicability to sequence learning problems. In particular, different methods for training HMMs on sequences and for combining their output are investigated in the context of automatic speech recognition. REPORT DimitrakBengio2003a/IDIAP Boosting HMMs with an application to speech recognition Dimitrakakis, Christos Bengio, Samy EXTERNAL http://publications.idiap.ch/attachments/reports/2003/rr03-41.pdf PUBLIC Idiap-RR-41-2003 2003 IDIAP Accepted for publication in ICASSP 2004 Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines its applicability to sequence learning problems. In particular, different methods for training HMMs on sequences and for combining their output are investigated in the context of automatic speech recognition.