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Speech Processing has been developed as one of the vital provision region of Digital Signal Processing. Speaker recognition is the methodology of immediately distinguishing who is talking dependent upon special aspects held in discourse waves. This strategy makes it conceivable to utilize the speaker's voice to check their character and control access to administrations, for example voice dialing, data administrations, voice send, and security control for secret information.A review on speaker recognition and emotion recognition is performed based on past ten years of research work. So far iari is done on text independent and dependent speaker recognition. There are many prosodic features of speech signal that depict the emotion of a speaker. A detailed study on these issues is presented in this paper.

In this paper, we investigate the enhancement of speech by applying kernel adaptive filter. Noise removal is very important in many applications like telephone conversation, speech recognition, etc. Kernel methods have shown good results for other applications like handwriting recognition, inverse distance weightings, etc. To improve the speech quality and intelligibility, we can process the signals in new domain like Reproducing Kernel Hilbert Space (RKHS) unlike time and frequency domains. We have used the noisy speech corpus (NOIZEUS) for the experiments. The experimental results shown the noise removal in RKHS has good improvement in the Signal to Noise Ratio (SNR) values as compared the traditional methods.

In the present day real time applications of visual object tracking in surveillance, it has become extremely complex, time consuming and tricky to do the tracking when there are occlusions are present for small duration or for longer time and also when it is done in outdoor environments. In these conditions, the target to be tracked can be lost for few seconds and that should be tracked as soon as possible. As from the literature it is observed that particle filter can be able to track the target robustly in different kinds of background conditions, and it's robust to partial occlusion. However, this tracking cannot recover from large proportion of occlusion and complete occlusion, to avoid this condition, we proposed two new algorithms (modified kalman and modified particle filter) for fast tracking of objects in the presence of occlusions. We considered the complete occlusion of tracking object and the main objective is how fast the system is able to track the object after the occlusion is crossed. From the experimental results, it is observed that the proposed algorithms have shown good improvement in results compared to the traditional methods. Index Terms-Visual object tracking, kalman filter, background substraction, particle filter.

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