Abstract
Our goal was to build a text-independent speaker recognition system that could be used under any conditions without any additional adaptation process. Unknown mismatched microphones and noise conditions can severely degrade the performance of speaker recognition systems. This paper shows that principal component analysis (PCA) can increase performance under these conditions without reducing dimension. We also propose a PCA process that augments class discriminative information sent to original feature vectors before PCA transformation and selects the best direction between each pair of highly confusable speakers. In tests, the proposed method reduced errors in recognition by 32%.
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Yu, HJ. (2005). Speaker Recognition in Unknown Mismatched Conditions Using Augmented PCA. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds) Computer and Information Sciences - ISCIS 2005. ISCIS 2005. Lecture Notes in Computer Science, vol 3733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569596_69
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DOI: https://doi.org/10.1007/11569596_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29414-6
Online ISBN: 978-3-540-32085-2
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