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.


Year:
2003
Publisher:
IDIAP
Keywords:
Note:
Accepted for publication in ICASSP 2004
Laboratories:




 Record created 2006-03-10, last modified 2018-03-17

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