In an effort to provide a more efficient representation of the speech signal, the application of the wavelet analysis is considered. This research presents an effective and robust method for extracting features for speech processing. Based on the time-frequency multi-resolution property of wavelet transform, the input speech signal is decomposed into various frequency channels. The major issues concerning the design of this Wavelet based speech recognition system are choosing optimal wavelets for speech signals, decomposition level in the DWT, selecting the feature vectors from the wavelet coefficients. More specifically automatic classification of various speech signals using the DWT is described and compared using different wavelets. Finally, wavelet based feature extraction system and its performance on an isolated word recognition problem are investigated. For the classification of the words, three layered feed forward network is used. General TermsDynamic Time Warping (DTW) Algorithm, Wavelet Transform (WT).

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Classification of textures in remotelysensed data has received considerable attention during the past decades. One difficulty of texture analysis in the past was lack of adequate tools to characterize different scales of textures effectively.Recent space-frequency analytical tools like the wavelet transform can effectively characterize the coupling of different scales in texture and helps to overcome the difficulty. This paper presents a wavelet-based texture classification technique that was applied to a Multi-Spectral Scanner (MSS) image of Madurai City, Tamil Nadu, India The feature extraction stage of the technique uses Lemarie-Battle orthogonal wavelets to derive a texture feature vector and this vector is input to a fuzzy-c means classifier, an unsupervised classification procedure. Four indices (user's accuracy, producer's accuracy, overall accuracy and Kappa co-efficient) are used to assess the accuracy of the classified data. The experiment results show that the performance of the presented technique is superior to the classical techniques.