Abstract
Automatic lip reading is a technique of understanding the uttered speech by visually interpreting the lip movement of the speaker. The two major parts, which play crucial role in lip reading system, are feature extraction followed by the classifier. For automatic lip reading, there are many competing methods published by researchers for feature extraction and classifiers. In this paper, we compare some of these leading methods. We have compared Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), K-Nearest Neighborhood (KNN), Random Forest Method (RFM) and Naive Bayes (NB) classifiers, on the basis of recognition performance and training time. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are studied to extract feature vectors. The CUAVE and Tulips database are used for experimentation and comparison. It is observed that SVM outperforms the rest for CUAVE database. Training time of SVM is also less than others.
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