DocumentCode
3348247
Title
Boosting HMMs with an application to speech recognition
Author
Dimitrakakis, Christos ; Bengio, Samy
Author_Institution
IDIAP, Martigny, Switzerland
Volume
5
fYear
2004
fDate
17-21 May 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, focusing on speech recognition. We apply boosting at the phoneme model level and recombine expert decisions using multi-stream techniques.
Keywords
hidden Markov models; learning (artificial intelligence); pattern classification; signal classification; speech recognition; AdaBoost algorithm; HMM boosting; classifier training; hidden Markov models; multi-stream techniques; phoneme model; sequence learning problems; speech recognition; Algorithm design and analysis; Boosting; Decision making; Hidden Markov models; Iterative algorithms; Learning systems; Machine learning; Machine learning algorithms; Management training; Speech recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on
ISSN
1520-6149
Print_ISBN
0-7803-8484-9
Type
conf
DOI
10.1109/ICASSP.2004.1327187
Filename
1327187
Link To Document