TY  - JOUR
T1  - Secure Mobile Learning System using Voice Authentication
AU - Aldeen Khairi, Teaba Wala 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 22
SP  - 8180
EP  - 8186
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.8180.8186
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.8180.8186
KW  - FAR
KW  -FRR
KW  -wavelet transform
KW  -HPSO
KW  -voice authentication
KW  -Mobile learning
KW  -CVR
INTRODUCTION
AB  - In the last decade, the demand for learning through mobile devices has been increased, however, the
security and authentication of these systems have less attention. This is because of researchers desirability
to be more famous by unauthenticated publishing of their articles. Therefore, this study presents a proposed
voice authentication for mobile learning (m-learning) system as a secure solution. In the proposed system, each
of the server and clients (learners) in the designed learning system is provided with voice features extraction
algorithm. HPSO algorithm is used for extraction the wavelet frequency domain features. These extracted
features are then matched with stored database in order to give the permission of learning system accessibility.
For (LL subband) FAR is 0.0, FRR is 0.01 and CVR is 99%. For (LL, LH, HL and HH) FAR is 0.0, FRR is 0.0 and
CVR is 100%. The voice recognition time is about 1.04 sec.
ER  -