Mazumder M.A.Salam R.A.2024-05-282024-05-2820192278309110.30534/ijatcse/2019/5481.320192-s2.0-85074136818https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074136818&doi=10.30534%2fijatcse%2f2019%2f5481.32019&partnerID=40&md5=a03d08deac9ec9c0e61dd0cae03a1db4http://www.warse.org/IJATCSE/static/pdf/file/ijatcse54813sl2019.pdfhttps://oarep.usim.edu.my/handle/123456789/8692In digital signal processing, speech processing is one of the areas that is used in many type of applications. It is one of an intensive field of research. The major criterion for good speech processing system is the selection of feature extraction technique, which plays a major role in achieving higher accuracy. In this paper, most commonly used techniques for feature extraction such as Linear Predictive Coefficient (LPC), Mel Frequency Cepstral Coefficient (MFCC), Perceptual Linear Prediction (PLP), Relative Spectral Perceptual Linear Prediction (RASTA-PLP) and Wavelet Transform (WT) are presented. Comparisons that highlight the strengths and the weaknesses of these techniques are also presented. Studies show that feature extraction techniques are mainly selected based on the requirement of the applications. Wavelet transform outperform other techniques for the analysis of non-stationary signals in audio signal. Enhanced Wavelet transform technique is a way forward and studies can be focused on its coefficients. Hybrid methods can be further explored to increase the performance in speech processing. A number of hybrid methods were reviewed, and studies show that Mel-Frequency Cepstral Coefficients (WPCC) provide better results for speech processing applications with standard coefficient for classification. � 2019, World Academy of Research in Science and Engineering. All rights reserved.en-USLinear Predictive Coefficient (LPC)Mel Frequency Cepstral Coefficient (MFCC)Perceptual Linear Prediction (PLP)Relative Spectral Perceptual Linear Prediction (RASTA-PLP)Wavelet Transform (WT)Article28529281.3 S154