In this paper our main aim to provide the difference between cepstral and non-cepstral feature extraction techniques. Here we try to cover-up most of the comparative features of Mel Frequency Cepstral Coefficient and prosodic features. In speaker recognition, there are two type of techniques are available for feature extraction: Short-term features i.e. Mel Frequency Cepstral Coefficient (MFCC) and long-term features (Prosodic) extraction techniques. In this paper, we explore the usefulness of prosodic features for syllable classification and MFCC for feature extraction of a speech signal followed by comparison between them. The Me1 Frequency Cepstral Coefficients (MFCC) is one of the most important features extraction techniques, which is required among various kinds of speech applications. The MFCC features are extracted from the speaker phonemes in the presegmented speech sentences. Now days Prosodic features are currently used in most emotion recognition algorithms Prosodic features are relatively simple in their structures and known for their effectiveness in some speech recognition tasks. There are various ways of generating prosodic syllable contour features that have recently been applied to enhance systems for speaker recognition.
General TermsSpeaker Recognition, Mel Frequency Cepstral Coefficient (MFCC), Prosodic.
Image Restoration is one of area related to image processing which deals with restoring an original and sharp image from corrupted image using a mathematical degradation and restoration model. In this proposed work, a comparative study analysis of simple, fast technique is given to remove noise of an image which is mostly introduced due to environmental changes or due to other issues. Researchers focus on the noise issues that changes image pixels value either on or off. To get an enough efficient method to remove the noise from the images is a greater challenge for the researchers. Noise plays an important role in degrading the image at the time of capturing or transmission of the image. There are many algorithms and filtering techniques available which have their own assumptions, merits and demerits depending upon the prior knowledge of the noise. Image smoothening is one of the most significant and widely used procedure in the image processing. Here, apart from noise a model, the light is also thrown on comparative analysis of noise removal techniques is done. This paper will present the different noise types to an image models and investigating the various noise reduction techniques and their advantages and disadvantages and also it will help the new researchers to have the detailed and comparative knowledge regarding image restoration and all its associated details.
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