Speech recognition using Adaboost HMM

Speech recognition using Adaboost HMM

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 recognit...

Full description

Saved in:
Bibliographic Details
Main Author: Ooi, Mun Siang
Other Authors: Foo Say Wei
Format: Theses and Dissertations
Language:English
Published: 2015
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Online Access:http://hdl.handle.net/10356/64891
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary: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.

Similar Items