Feature extraction based on LDAO algorithm in lipreading
一种基于LDAO的唇读特征提取算法

In speech and lipreading recognition application, LDAlinear discriminant analysisalgorithm is usually based on syllable, semi-syllable, HMM state or other class units. But the extracted features based on traditional LDA have no direct relation to recognition accuracy. This paper proposed linear discriminant analysis based on objectLDAO algorithm on recognizing isolated words in lipreading. It selected objects to be recognized as class to LDA, which ensured feature extracting followed the most discriminant directions among objects in theory. Experiments on bimodal database show that this algorithm is superior to any other feature extracting algorithms in lipreading. Specifically, the recognition accuracy is better than DCT+LDA algorithm about 3%.