A Survey Paper on Gender Identification System using Speech Signal
Mohit Kumar Mishra1, Arun Kumar Shukla2
1Mohit Kumar Mishra, Department of Computer Science & Information Technology, Sam Higginbottom Institute of Agriculture, Technology and Sciences, Naini, Allahabad (U.P), India.
2Arun Kumar Shukla, Department of Computer Science & Information Technology, Sam Higginbottom Institute of Agriculture, Technology and Sciences, Naini, Allahabad (U.P), India.
Manuscript received on 10 August 2017 | Revised Manuscript received on 18 August 2017 | Manuscript Published on 30 August 2017 | PP: 165-167 | Volume-6 Issue-6, August 2017 | Retrieval Number: F5158086617/17©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Gender is a critical statistic characteristic of individuals. This paper provides a survey of automatic human gender identification using speech signal characteristics and classifiers. A review of approaches exploiting information from human speech presented. Here, highlights of selection of speech features, their processing and different classifiers used for this purpose are discussed. Based on the results discussed in the papers it can be stated as, accuracy of automatic gender identification system with any classifiers is better if speech dataset used for training and testing is taken/ recorded in the same environments. Pitch is the basic feature of speech which distinguishes between adult man and woman. Other features like MFCC, LPC, RASTA-PLP also used for automatic gender identification. Neural Network, Support Vector Machine (SVM), Random Forest etc. are used for automatic gender identification through speech signal. Till now, many challenges are still available here to identify gender with acceptable accuracy in real life environmental speech where noise is acoustically added with human speech.
Keywords: Gender Identification, MFCC, SVM.
Scope of the Article: Signal and Speech Processing