Boosting HMMs with an application to speech recognition
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.
Accepted for publication in ICASSP 2004
Record created on 2006-03-10, modified on 2016-08-08