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Considering Speech Quality in Speaker Verification Fusion AbstractThis paper emphasizes the benefits of embedding data categorization within fusion of classifiers for text-independent speaker verification. A selective fusion framework is presented which considers data idiosyncrasies by assigning particular test samples to appropriate fusion schemes. As an extension, incompatible data can be spotted and excluded from inherent classification errors. In addition, it’s shown that multi-resolution low-level classifiers successfully boost fusion capabilities in noise.
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