Date Approved

3-15-2017

Embargo Period

3-15-2017

Document Type

Thesis

Degree Name

MS Electrical and Computer Engineering

Department

Electrical and Computer Engineering

College

Henry M. Rowan College of Engineering

First Advisor

Ramachandran, Ravi P.

Second Advisor

Schmalzel, John

Third Advisor

Thayasivam, Umashanger

Subject(s)

Automatic speech recognition; Speech processing systems

Disciplines

Electrical and Computer Engineering

Abstract

In the presence of environmental noise, speaker verification systems inevitably see a decrease in performance. This thesis proposes the use of two parallel classifiers with several enhancement methods in order to improve the performance of the speaker verification system when noisy speech signals are used for authentication. Both classifiers are shown to receive statistically significant performance gains when signal-to-noise ratio estimation, affine transforms, and score-level fusion of features are all applied. These enhancement methods are validated in a large range of test conditions, from perfectly clean speech all the way down to speech where the noise is equally as loud as the speaker. After each classifier has been tuned to their best configuration, they are also fused together in different ways. In the end, the performances of the two classifiers are compared to each other and to the performances of their fusions. The fusion method where the scores of the classifiers are added together is found to be the best method.

Other Repository URL

http://dissertations.umi.com/rowan:10269

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