Speech Recognition for Urdu language is an interesting and less developed task. This is primarily due to the fact that linguistic resources such as rich corpus are not available for Urdu. Yet, few attempts have been made for developing Urdu speech recognition frameworks using the traditional approaches such as Hidden Markov Models and Neural Networks. In this work, we investigate the use of three classification methods for Urdu speech recognition task. We extract the Mel Frequency Cepstral Coefficients, the delta and delta-delta features from the speech data and train the classifiers to perform Urdu speech recognition. We present the performance achieved by training a Support Vector Machine (SVM) classifier, a random forest (RF) classifier and a linear discriminant analysis classifier (LDA) for comparison with SVM. Consequently, the experimental results show that SVM gives better performance than RF and LDA classifiers on this particular task.
Abstract. We apply the complex wavelet structural similarity index to image matching system and propose an image matching method which has strong robustness to image transform in spatial domain. Experimental results show that the structural similarity index in complex wavelet domain reflects to a large extent structural similarity of the images compared, which is more similar to human visual cognitive system; in the meanwhile, because of approximate shift invariance of complex wavelet, this index shows good robustness to such disturbance as contrast ratio change and illumination change to template image, so it is more suitable to be used as similarity index for image matching under complex imaging conditions. Moreover, matching simulation experiment shows that this method has higher correct matching rate in complicated disturbance environment.
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