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Even though Support Vector Machines (SVMs) are capable of identifying patterns in high dimensional spaces that are presented by kernels without a computational decrease, their performance is determined by two main factors: SVM cost param- eter and kernel parameters. It is empirically proven that the optimum parameter combination for both SVM cost a...
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We address the problem of categorising documents using kernel-based methods such as Support Vector Machines. Since the work of T. Joachims [“Text categorization with support vector machines: Learning with many relevant features”, Lect. Notes Comput. Sci. 1398, 137–142 (1998)], there is ample experimental evidence that SVM using the standard word fr...
This paper presents a new class of 2D string kernels, called spatial mismatch kernels, for use with support vector machine (SVM) in a discriminative approach to the image categorization problem. We first represent images as 2D sequences of those visual keywords obtained by clustering all the blocks that we divide images into on a regular grid. Thro...
This paper presents a new class of 2D string kernels, called spatial mismatch kernels, for use with support vector machine (SVM) in a discriminative approach to the image categorization problem. We first represent images as 2D sequences of those visual keywords obtained by clustering all the blocks that we divide images into on a regular grid. Thro...
Citations
... Although SVMs are capable of pattern classification in a high dimensional space using kernels, their performance is determined by three main factors: kernel selection, the SVM cost parameter and kernel parameters [7][8][9]. Many researchers have committed considerable time to finding the optimum kernel functions for speaker recognition [10][11][12] due to the diverse sets of kernel functions available. ...
Sparse representation-based methods have very lately shown promise for speaker recognition systems. This paper investigates and develops an i-vector based sparse representation classification (SRC) as an alternative classifier to support vector machine (SVM) and Cosine Distance Scoring (CDS) classifier, producing an approach we term i-vector-sparse representation classification (i-SRC). Unlike SVM which fixes the support vector for each target example, SRC allows the supports, which we term sparse coefficient vectors, to be adapted to the test signal being characterized. Furthermore, similarly to CDS, SRC does not require a training phase. We also analyze different types of sparseness methods and dictionary composition to determine the best configuration for speaker recognition. We observe that including an identity matrix in the dictionary helps to remove sensitivity to outliers and that sparseness methods based on ℓ1 and ℓ2 norm offer the best performance. A combination of both techniques achieves a 18% relative reduction in EER over a SRC system based on ℓ1 norm and without identity matrix. Experimental results on NIST 2010 SRE show that the i-SRC consistently outperforms i-SVM and i-CDS in EER by 0.14–0.81%, and the fusion of i-CDS and i-SRC achieves a relative EER reduction of 8–19% over i-SRC alone.
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 different 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 (GMM-SRC) 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.
