Browse DORAS
Browse Theses
Latest Additions
Creative Commons License
Except where otherwise noted, content on this site is licensed for use under a:

A PCA based manifold representation for visual speech recognition

Yu, Dahai and Ghita, Ovidiu and Sutherland, Alistair and Whelan, Paul F. (2007) A PCA based manifold representation for visual speech recognition. In: CIICT 2007 - Proceedings of the China-Ireland International Conference on Information and Communications Technologies, 28-29 August 2007, Dublin, Ireland.

Full text available as:

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


In this paper, we discuss a new Principal Component Analysis (PCA)-based manifold representation for visual speech recognition. In this regard, the real time input video data is compressed using Principal Component Analysis and the low-dimensional points calculated for each frame define the manifold. Since the number of frames that form the video sequence is dependent on the word complexity, in order to use these manifolds for visual speech classification it is required to re-sample them into a fixed pre-defined number of key-points. These key-points are used as input for a Hidden Markov Model (HMM) classification scheme. We have applied the developed visual speech recognition system to a database containing a group of English words and the experimental data indicates that the proposed approach is able to produce accurate classification results.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Additional Information:Microsoft Best Paper Award
Uncontrolled Keywords:Visual speech recognition; PCA manifolds; spline interpolation; Hidden Markov Model;
Subjects:Computer Science > Digital video
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Official URL:
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:281
Deposited On:11 Mar 2008 by DORAS Administrator. Last Modified 03 Feb 2009 14:38

Download statistics

Archive Staff Only: edit this record