Evaluation for GMM-UBM and 3D Convolutional Neural Networks Systems
نام ساير پديدآوران
Boult, Terrance
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
University of Colorado Colorado Springs
تاریخ نشرو بخش و غیره
2019
مشخصات ظاهری
نام خاص و کميت اثر
73
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
M.Eng.
کسي که مدرک را اعطا کرده
University of Colorado Colorado Springs
امتياز متن
2019
یادداشتهای مربوط به خلاصه یا چکیده
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.
موضوع (اسم عام یاعبارت اسمی عام)
موضوع مستند نشده
Computer science
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )