CLOUD COMPUTING: FUTURE PROSPECT FOR E-VOTING
Published In: SECOND INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRONICS, ELECTRICAL AND COMPUTER ENGINEERING
Author(s): ABDUL MATEEN ANSARI , AFTAB ALAM , MD. AFTAB ALAM
Abstract: In the present era, internet brought drastic change on the peoples’ lives. It made life easy by providing information on click. Today one can just logon to internet for entertainment, jobs hunting, education, news, shopping, health suggestions, learn cosmetic techniques, food recipes and more on the interest of the individuals. Inline to the people affinity towards internet the government started shifting their elections activities over the internet; called as e-voting .the main purpose of e-voting is to enhance transparency in electoral process and rejuvenating democracy to the reach of one and all. Cloud computing has been evolved as an effective computing paradigm offer digital resource to access and maintain IT resources for optimum utilization at affordable cost. To inherit alluring features of cloud many governments are determine to shift their Election activities on the cloud. This paper aims to present an electronic voting system (E-Voting).In this research we shall investigate
- Publication Date: 13-Jun-2013
- DOI: 10.15224/978-981-07-6935-2-30
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MULTI-DEVICES HINDI SPEECH DATABASE FOR SPEAKER IDENTIFICATION USING GMM
Published In: SECOND INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRONICS, ELECTRICAL AND COMPUTER ENGINEERING
Author(s): MAHESH CHANDRA , SONU KUMAR
Abstract: In this paper, we study the effect on speaker identification (SI) system when speech data is recorded on two different sensors, a HP Pavilion third generation laptop and a Samsung mobile ( S3770K) both with built-in microphone in parallel in a closed room in noise free environment. The database contains 10 Hindi sentences (50-60 seconds speech) and one english sentence (7-8 seconds speech) of each 39 speakers (26 Male and 13 Female) in a reading style manner. Identification process adopts the methods of feature extraction based on Mel-frequency cepstrum coefficients (MFCC), linear predictive coding (LPC) coefficients. Gaussian mixture model (GMM) is used as a classifier. Our study shows that higher degradation in performance in case of mismatch of sensors during training and testing of data and MFCC performs better during matched conditions, LPC performs better than MFCC in mismatched conditions.
- Publication Date: 13-Jun-2013
- DOI: 10.15224/978-981-07-6935-2-31
- Views: 0
- Downloads: 0