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.


Published in:
IEEE International Conference on Acoustic, Speech, and Signal Processing, ICASSP
Presented at:
IEEE International Conference on Acoustic, Speech, and Signal Processing, ICASSP
Year:
2004
Keywords:
Note:
IDIAP-RR 03-41
Laboratories:




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

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