عنوان

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

شماره

TL50176

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

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

نام عام مواد

[Thesis]

نام نخستين پديدآور

Alghamdi, Mohammad S.

نام ساير پديدآوران

Boult, Terrance

نام ناشر، پخش کننده و غيره

University of Colorado Colorado Springs

تاریخ نشرو بخش و غیره

2019

متن يادداشت

73 p.

جزئيات پايان نامه و نوع درجه آن

M.Eng.

کسي که مدرک را اعطا کرده

University of Colorado Colorado Springs

امتياز متن

2019

متن يادداشت

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.

اصطلاح موضوعی

Computer science

مستند نام اشخاص تاييد نشده

Alghamdi, Mohammad S.

مستند نام اشخاص تاييد نشده

Boult, Terrance

مستند نام تنالگان تاييد نشده

University of Colorado Colorado Springs

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y