Publication:
Feature extraction techniques for speech processing: A review

Simple item page
dc.FundingDetailsUniversiti Sains Islam Malaysia,�USIM: PPP/UTG-0114/FST/30/11414
dc.FundingDetailsThe authors would like to express their gratitude to Universiti Sains Islam Malaysia (USIM) for the supports and facilities provided. This research study is sponsored by Universiti Sains Islam Malaysia (USIM) under USIM Competitive Grant [PPP/UTG-0114/FST/30/11414].
dc.contributor.affiliationsIslamic Science Institute
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorMazumder M.A.en_US
dc.contributor.authorSalam R.A.en_US
dc.date.accessioned2024-05-28T08:25:55Z
dc.date.available2024-05-28T08:25:55Z
dc.date.issued2019
dc.description.abstractIn 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.
dc.description.natureFinalen_US
dc.identifier.ArtNo54
dc.identifier.doi10.30534/ijatcse/2019/5481.32019
dc.identifier.epage292
dc.identifier.issn22783091
dc.identifier.issue1.3 S1
dc.identifier.scopus2-s2.0-85074136818
dc.identifier.spage285
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85074136818&doi=10.30534%2fijatcse%2f2019%2f5481.32019&partnerID=40&md5=a03d08deac9ec9c0e61dd0cae03a1db4
dc.identifier.urihttp://www.warse.org/IJATCSE/static/pdf/file/ijatcse54813sl2019.pdf
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/8692
dc.identifier.volume8
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherWorld Academy of Research in Science and Engineeringen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Advanced Trends in Computer Science and Engineering
dc.subjectLinear Predictive Coefficient (LPC)en_US
dc.subjectMel Frequency Cepstral Coefficient (MFCC)en_US
dc.subjectPerceptual Linear Prediction (PLP)en_US
dc.subjectRelative Spectral Perceptual Linear Prediction (RASTA-PLP)en_US
dc.subjectWavelet Transform (WT)en_US
dc.titleFeature extraction techniques for speech processing: A reviewen_US
dc.title.alternativeInt. J. Adv. Trends Comput. Sci. Eng.en_US
dc.typeArticleen_US
dspace.entity.typePublication

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Feature extraction techniques for speech processing A review.pdf
Size:
285.89 KB
Format:
Adobe Portable Document Format
Download

Collections

Scopus