عنوان

Speaker Recognition: Evaluation for GMM-UBM and 3D Convolutional Neural Networks Systems

Number

TL50176

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Speaker Recognition: Evaluation for GMM-UBM and 3D Convolutional Neural Networks Systems

General Material Designation

[Thesis]

First Statement of Responsibility

Alghamdi, Mohammad S.

Subsequent Statement of Responsibility

Boult, Terrance

Name of Publisher, Distributor, etc.

University of Colorado Colorado Springs

Date of Publication, Distribution, etc.

2019

Text of Note

73 p.

Dissertation or thesis details and type of degree

M.Eng.

Body granting the degree

University of Colorado Colorado Springs

Text preceding or following the note

2019

Text of Note

The Speaker Recognition (SR) systems are more accurate than ever in verifying and identifying the human voice which is one of the most convenient biometric characteristics of the human identity. Research and development on speaker recognition techniques have been varied widely in the last decade with an aim to lessen relevant challenges effects such as background noise, poor channel conditions, crosstalk, etc. In this paper, we evaluate two speaker verification (SV) systems, and each one uses an entirely different method to verify speakers: 1) ALIZE 3.0 which is an opensource platform for SR that was successfully passed the NIST Speaker Recognition Evaluations (SREs) implementing Gaussian mixture model (GMM)-UBM speaker. The other one is 2) 3D Convolutional Neural Network (3D-CNN) architecture, which uses a novel method for speaker verification based on Neural Network technique. This paper investigates how challenging it is to implement applications handling tasks in the field of speaker verification.

Subject Term

Computer science

Alghamdi, Mohammad S.

Boult, Terrance

University of Colorado Colorado Springs

Electronic name

p

[Thesis]

276903

a

Y