Abstract
This paper presents an improved speaker verification technique that is especially appropriate for surveillance scenarios. The main idea is a meta-learning scheme aimed at improving fusion of low- and high-level speech information. While some existing systems fuse several classifier outputs, the proposed method uses a selective fusion scheme that takes into account conveying channel, speaking style and speaker stress as estimated on the test utterance. Moreover, we show that simultaneously employing multi-resolution versions of regular classifiers boosts fusion performance. The proposed selective fusion method aided by multi-resolution classifiers decreases error rate by 30% over ordinary fusion.
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Solewicz, Y.A., Koppel, M. (2005). Selective Fusion for Speaker Verification in Surveillance. In: Kantor, P., et al. Intelligence and Security Informatics. ISI 2005. Lecture Notes in Computer Science, vol 3495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427995_22
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DOI: https://doi.org/10.1007/11427995_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25999-2
Online ISBN: 978-3-540-32063-0
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