Selecting relevant visual features for speechreading

Authors:
V. Estellers;M. Gurban;J. P. Thiran
Affiliations:
Ecole Polytechnique Fédérale de Lausanne, Signal Processing Laboratory 5, Lausanne, Switzerland;Ecole Polytechnique Fédérale de Lausanne, Signal Processing Laboratory 5, Lausanne, Switzerland;Ecole Polytechnique Fédérale de Lausanne, Signal Processing Laboratory 5, Lausanne, Switzerland
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 4
Cited 0

Elements of information theory

Elements of information theory
Feature Selection for Knowledge Discovery and Data Mining

Feature Selection for Knowledge Discovery and Data Mining
Fast Binary Feature Selection with Conditional Mutual Information

The Journal of Machine Learning Research
Moving-talker, speaker-independent feature study, and baseline results using the CUAVE multimodal speech corpus

EURASIP Journal on Applied Signal Processing

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Abstract

A quantitative measure of relevance is proposed for the task of constructing visual feature sets which are at the same time relevant and compact. A feature's relevance is given by the amount of information that it contains about the problem, while compactness is achieved by preventing the replication of information between features. To achieve these goals, we use mutual information both for assessing relevance and measuring the redundancy between features. Our application is speechreading, that is, speech recognition performed on the video of the speaker. This is justified by the fact that the performance of audio speech recognition can be improved by augmenting the audio features with visual ones, especially when there is noise in the audio channel. We report significant improvements compared to the most common method of dimensionality reduction for speechreading, Linear Discriminant Analysis (LDA).