Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSirajudeen Gulam Razul.en_US
dc.description.abstractThis thesis describes the development of a robust automatic speaker verification system (ASV) with specific interest in the extraction of dominant acoustic features. Our primary investigation involves the development of robust feature extraction techniques to improve the performance of the system under noisy conditions. By far, the most widely used feature in this area is the Mel Frequency Cepstral Coefficients (MFCC). The techniques developed here are processing strategies, which improves the MFCC feature set. We have introduced four techniques to improve the robustness of the system against noise, particularly additive white Gaussian noise (AWGN). The first three are integrated processing strategies and the last one a pre-processing technique. These features are subsequently used to train a speaker model which eventually is used to represent a particular speaker. The model that we have selected is the Gaussian Mixture Model (GMM). This model is used as opposed to the Hidden Markov Model (HMM) because of its simplicity and fast processing time.en_US
dc.format.extent122 p.en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Biometricsen_US
dc.titleFeature extraction in speaker verification under noisy conditionsen_US
dc.contributor.supervisorKot, Alex Chichungen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Engineeringen_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Theses
Files in This Item:
File Description SizeFormat 
  Restricted Access
Main report15.04 MBAdobe PDFView/Open

Page view(s)

Updated on Aug 13, 2022


Updated on Aug 13, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.