PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Boosting HMMs with an application to Speech Recognition
Christos Dimitrakakis and Samy Bengio
In: ICASSP 2004, US(2004).

Abstract

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.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Speech
Theory & Algorithms
ID Code:535
Deposited By:Christos Dimitrakakis
Deposited On:25 December 2004