DocumentCode
2006346
Title
Bimodal Speech Recognition Based on Hierarchical Parallel Boosting
Author
Qin Wei ; Wei Gang ; Yu Wei-Yu
Author_Institution
South China Univ. of Technol., Guangzhou
fYear
2007
fDate
May 30 2007-June 1 2007
Firstpage
1752
Lastpage
1755
Abstract
In this paper, a weak learning algorithm is boosted to a strong effective learning algorithm using the boosting algorithm. The traditional boosting algorithm costs a great deal of running time. In order to decrease the running time, a hierarchical parallel boosting algorithm is proposed, which supports the multi-class classifying problem. Some simple classifiers using this algorithm can be trained in parallel. In addition, within one classifier, models for every class can be trained in parallel, too. Experimental results of bimodal speech recognition show that the new algorithm is able to produce classifiers as accurate as the traditional boosting classifier with the same number of base classifiers, but with greatly reduced running time.
Keywords
hidden Markov models; learning (artificial intelligence); signal classification; speech recognition; bimodal speech recognition; hidden Markov model; hierarchical parallel boosting; multiclass classifying problem; weak learning algorithm; Acoustic noise; Acoustical engineering; Automatic control; Automation; Boosting; Costs; Hidden Markov models; Loudspeakers; Speech recognition; Voting; Bimodal Speech Recognition; Hidden Markov Model; Parallel Boosting;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Automation, 2007. ICCA 2007. IEEE International Conference on
Conference_Location
Guangzhou
Print_ISBN
978-1-4244-0817-7
Type
conf
DOI
10.1109/ICCA.2007.4376661
Filename
4376661
Link To Document