Please use this identifier to cite or link to this item:
Title: Speech recognition using Adaboost HMM
Authors: Ooi, Mun Siang
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Issue Date: 2005
Abstract: For speech recognition, Hidden Markov Model (HMM) is a popular approach as the classifier with high degree of accuracy; Adaptive Boosting (Adaboost) is a method to improve the performance of a given base classifier. In this study, Adaboost technique is applied to HMM classifier in speech recognition to test the resulting performance. Experiments on several speech corpora showed that Adaboost-HMM classifiers are significantly more accurate than the baseline HMM classifiers. Results also showed that sufficient training samples that cover most of the entire sample space is necessary for generalization of Adaboost-HMM classifiers.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
  Restricted Access
13.34 MBAdobe PDFView/Open

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.