Graduation Semester and Year

2019

Language

English

Document Type

Thesis

Degree Name

Master of Science in Civil Engineering

Department

Civil Engineering

First Advisor

Ming Li

Abstract

In recent years we have seen a variety of approaches to increase security on computers and mobile devices including fingerprint, and facial recognition. Such techniques while effective are very expensive. Voice biometrics, specifically speech rhythm, is a method that has been drawing attention and growing in recent years. Unlike other methods, it requires little to no additional hardware installed on a device for it to work accurately. Speech rhythm utilizes the device's built-in microphone, and analyzes speakers based on features of their speech. In this work we leverage the existing hardware and simply add an efficient layer of software to achieve user authentication. When the user speaks a passphrase, voice features are extracted and passed on to a neural network that analyzes those features and classifies whether the speaker is a recognized user or not. The reduced cost, coupled with the efficiency of speech rhythm makes it appealing to a variety of devices, as well as large base of users. 13 users participated in this study and yielded 93.3% accuracy. The results are robust and show a lot of promise for future work.

Keywords

Speech-rhythm, User syndication, Neural networks, Security, Computer science

Disciplines

Civil and Environmental Engineering | Civil Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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