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In the present paper, we suggest an implementation of an automatic understanding system of the statement in human-machine communication. The architecture we adopt is based on a stochastic approach that assumes that the understanding of a statement is nothing but a simple theme identification process. Therefore, we present a new theme identification method based on a documentary retrieval technique which is text (document) classification (Bawakid and Oussalah, 2010). The method we suggest was validated on a basic platform that gives information related to university schooling management (querying a student database), taking into consideration a textual input in French. This method has achieved a theme identification rate of 95% and a correct utterance understanding rate of about 91.66%.

Proposed is an efficient face recognition algorithm using the discrete cosine transform DCT Technique for reducing dimensionality and image parameterization. These DCT coefficients are examined by a MLP (Multi-Layer Perceptron) and radial basis function RBF neural networks. Their purpose is to present a face recognition system that is a combination of discrete cosine transform (DCT) algorithm with a MLP and RBF neural networks. Neural networks have been widely applied in pattern recognition for the reason that neural-networks-based classifiers can incorporate both statistical and structural information and achieve better performance than the simple minimum distance classifiers. The authors demonstrate experimentally that when DCT coefficients are fed into a back propagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. Comparison with other statistical methods like Principal component Analysis (PCA) and Linear Discriminant Analysis (LDA) is presented. Their face recognition system is tested on the computer vision science research projects and the ORL database.

Visemes are the unique facial positions required to produce phonemes, which are the smallest phonetic unit distinguished by the speakers of a particular language. Each language has multiple phonemes and visemes, and each viseme can have multiple phonemes. However, current literature on viseme research indicates that the mapping between phonemes and visemes is many-to-one: there are many phonemes which look alike visually, and hence they fall into the same visemic category. To evaluate the performance of the proposed method, the authors collected a large number of speech visual signal of five Algerian speakers male and female at different moments pronouncing 28 Arabic phonemes. For each frame the lip area is manually located with a rectangle of size proportional to 120*160 and centred on the mouth, and converted to gray scale. Finally, the mean and the standard deviation of the values of the pixels of the lip area are computed by using 20 images for each phoneme sequence to classify the visemes. The pitch analysis is investigated to show its variation for each viseme.