Graduation Semester and Year




Document Type


Degree Name

Master of Science in Computer Science


Computer Science and Engineering

First Advisor

Ming Li


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.


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


Computer Sciences | Physical Sciences and Mathematics


Degree granted by The University of Texas at Arlington