The most important challenges in AVSR and the focus of most research are the features that are extracted, and when combined give better results. The other challenge is the resulted feature here of nature are large in size, then prefers here to reduce the features by use of an appropriate way to reduce these data with ensure have their properties after downsizing. The System that is presented in this research is for recognition a group of Arabic words voices, from one to ten words. In the acoustic parts the features were extracted of coefficients MFCC, LPC,FFT to be determine which type of these features is efficient in AVSR .All these types of feature are showed efficient results but MFCC is the best. The visual features are calculated of DCT matrix, and the features are extracted by applying the zigzag scan. In the reduction features stage, several methods of data reducing have been implemented; they are LDA, PCA and SVD. Each method are applied to the data separately. The KNN models are used in the stage of recognition, where the testing is implemented on dependent and independent database of words from one to ten. The final results that obtained are efficient and encouraging.
It is very important to accurately track the mouth of a talking person for many applications, such as face recognition and human computer interaction. This is in general a difficult problem due to the complexity of shapes, colors, textures, and changing lighting conditions. In this paper we develop techniques for outer and inner lip tracking. From the tracking results FAPs are extracted which are used to drive an MPEG-4 decoder. A novel method consisting of a Gradient Vector Flow (GVF) snake with a parabolic template as an additional external force is proposed. Based on the results of the outer lip tracking, the inner lip is tracked using a similarity function and a temporal smoothness constraint. Numerical results are presented using the Bernstein database.
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