Improving automatic speaker verification using front-end and back-end diversity

350 words maximum: (PLEASE TYPE) Technologies that exploit biometrics can potentially be applied to the identification and verification of individuals for controlling access to secured areas or materials. Among these technologies, automatic speaker verification systems are of growing interest, as they are the least invasive and they allow recognition via any type of communication network over long distances. The overall goal of this thesis is to improve the performance of automatic speaker verification systems by investigating novel features and classification methods that complement current state-of-the-art systems. At the feature level, novel log-compressed least squares group delay and spectral centroid features are proposed. The log-compression and least squares regularisation are shown to reduce the dynamic range of modified group delay features and outperform other existing group delay extraction methods. The proposed spectral centroid features provide a better characterisation of spectral energy distribution and experimental results show that the detailed spectral characterisation significantly improves performance. A diverse front-end involving multiple features would improve both phonetic (acoustic) and speaker modelling. In this regard, the relative contributions of the acoustic and speaker modelling ‘stages’ on the speaker recognition performance across d ifferent features are investigated. The investigation conducted through the use of clustering comparison measures suggests that front-end diversity, and hence improved performance from fused systems, can be achieved purely through different ‘partitioning’ of the acoustic space. Built on the finding, a novel universal background model (UBM) data/utterance selection algorithm that increases stability of the acoustic modelling is proposed. Finally, at the classification level, the use of the sparse representation classification (SRC) using Gaussian mixture model supervectors (GMMSRC) is proposed and is found to perform comparably to Gaussian mixture model-support vector machines (GMM-SVM). However, GMM-SRC results in a slower verification process. In order to increase the computation efficiency, the large dimensional supervectors are replaced with speaker factors resulting in the joint factor analysis-sparse representation classification (JFA-SRC). In addition, a novel dictionary composition technique to further improve the computation efficiency is developed. Results demonstrate that the refined dictionary provide comparable performance over the use of the complete dataset and generalises well to the evaluation on other databases. Notably, a detailed comparison of the proposed JFA-SRC across various state-of-the-art classifiers on the NIST 2010 databases showed that the proposed JFA-SRC achieved the best Minimum Detection Cost Function (minDCF), highlighting the usefulness of the SRC-based systems. Declaration relating to disposition of project thesis/dissertation I hereby grant to the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all property rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstracts International (this is applicable to doctoral theses only). Signature . Witness . Date The University recognises that there may be exceptional circumstances requiring restrictions on copying or conditions on use. Requests for restriction for a period of up to 2 years must be made in writing. Requests for a longer period of restriction may be considered in exceptional circumstances and require the approval of the Dean of Graduate Research. FOR OFFICE USE ONLY Date of completion of requirements for Award: THIS SHEET IS TO BE GLUED TO THE INSIDE FRONT COVER OF THE THESIS ORIGINALITY STATEMENT ‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged. ’ Signed . Date . COPYRIGHT STATEMENT ‘I hereby grant the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstract International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation. ' Signed . Date . AUTHENTICITY STATEMENT ‘I certify that the Library deposit digital copy is a direct equivalent of the final officially approved version of my thesis. No emendation of content has occurred and if there are any minor variations in formatting, they are the result of the conversion to digital format. ’ Signed . Date .