International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Science , Computer Science and Engineering

Volume4,March 2017,

Topic : Secure voice based authentication for future IOT applications

Authors:Mr.K.Ravi kishore || Dr.M.Humera Khanam, Ms.D.Sucharitha, Ms.G.Divya

Abstract:Internet of Things (IOT) is a network of all devices that can be accessed through the internet. Using existing network infrastructure, the devices can be remotely accessed and controlled, thus allowing a direct integration of computing systems with the physical world. It also reduces human involvement along with improving accuracy, efficiency and resulting in economic benefit. The IOT devices facilitate the day to day life of people. However, due to its heterogeneous and dynamic nature the IOT has an enormous threat to security and privacy in IOT environment the Authentication is one of the most challenging security requirement, where a user can directly access information from the devices, provided the mutual authentication between user and devices happens. A system that recognizes and authenticates the voice of a user by extracting the distinct features of their voice samples is usually termed as Voice recognition system. Converting the human voice into digital data can be carried out by Voice identification. The digitized audio samples then undergo feature excerption process to extract Mel Frequency Cepstral Coefficients (MFCC) features. These coefficients are subjected to feature matching through Dynamic Time Warping (DTW) to match with the patterns existing in the database for limited language words. In this paper we provide the IOT applications to the voice based authentication by providing a natural language processing interface. For better performance we can combine the digital and mathematical knowledge using MFCC and DTW to extract and match the features to improve the accuracy.

Keywords: MFCC (Mel Frequency cepstral coefficients), DTW (Dynamic Time Warping)

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DOI: 01.1617/vol4/iss3/pid72659

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Dr. Allon Guez
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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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Journal Code : IJERCSE
Electronic ISSN : 2394-2320
Impact Factor : 4.890
Frequency : monthly
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