A boosted multi-HMM classifier for recognition of visual speech elements
A novel boosted classifier using multiple Hidden Markov Models (HMMs) is reported in this paper. The composite HMMs are specially trained to highlight certain group of training samples with the application of adaptive boosting technique. Experiments were carried out to identify the ba...
Saved in:
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2009
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/90750 http://hdl.handle.net/10220/4590 http://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:PUBMED&id=doi:10.1080/02699200400026884&genre=&isbn=&issn=0269-9206&date=&volume=20&issue=2-3&spage=149&epage=56&aulast=Parker&aufirst=%20Mark&auinit=&title=Clin%20Linguist%20Phon&atitle=Automatic%20speech%20recognition%20and%20training%20for%20severely%20dysarthric%20users%20of%20assistive%20technology%3A%20the%20STARDUST%20project%2E |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | A novel boosted classifier using multiple Hidden Markov Models (HMMs) is reported in this paper. The composite HMMs are specially trained to highlight certain group of training samples with the application of
adaptive boosting technique. Experiments were carried out to identify the basic visual speech elements in English
using the proposed boosted classifier. Comparing the results obtained using the proposed classifier and those
obtained using the traditional single HMM classifier, it may be said that the proposed system is significantly better in terms of accuracy and robustness. |
|---|