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An introduction to support Vector Machines: and other kernel-based learning methods
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Analysis of the Utility of Classical and Novel Speech Quality Measures for Speaker Verification
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Issues in stacked generalization
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An overview of text-independent speaker recognition: From features to supervectors
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Q-stack: uni- and multimodal classifier stacking with quality measures
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
GMM-SVM Kernel with a Bhattacharyya-based distance for speaker recognition
IEEE Transactions on Audio, Speech, and Language Processing
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IEEE Transactions on Audio, Speech, and Language Processing
P.563—The ITU-T Standard for Single-Ended Speech Quality Assessment
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
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IEEE Transactions on Audio, Speech, and Language Processing
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A challenge in automatic speaker verification is to create a system that is robust to the effects of vocal ageing. To observe the ageing effect, a speaker's voice must be analysed over a period of time, over which, variation in the quality of the voice samples is likely to be encountered. Thus, in dealing with the ageing problem, the related issue of quality must also be addressed. We present a solution to speaker verification across ageing by using a stacked classifier framework to combine ageing and quality information with the scores of a baseline classifier. In tandem, the Trinity College Dublin Speaker Ageing database of 18 speakers, each covering a 30-60 year time range, is presented. An evaluation of a baseline Gaussian Mixture Model-Universal Background Model (GMM-UBM) system using this database demonstrates a progressive degradation in genuine speaker verification scores as ageing progresses. Consequently, applying a conventional threshold, determined using scores at the time of enrolment, results in poor long-term performance. The influence of quality on verification scores is investigated via a number of quality measures. Alongside established signal-based measures, a new model-based measure, Wnorm, is proposed, and its utility is demonstrated on the CSLU database. Combining ageing information with quality measures and the scores from the GMM-UBM system, a verification decision boundary is created in score-ageing-quality space. The best performance is achieved by using scores and ageing in conjunction with the new Wnorm quality measure, reducing verification error by 45% relative to the baseline. This work represents the first comprehensive analysis of speaker verification on a longitudinal speaker database and successfully addresses the associated variability from ageing and quality arte-facts.