@inproceedings{7f1344ea8fcf4ee28ce9921571641168,
title = "Speaker recognition in unknown mismatched conditions using augmented PCA",
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\%.",
author = "Yu, \{Ha Jin\}",
year = "2005",
doi = "10.1007/11569596\_69",
language = "English",
isbn = "3540294147",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "668--676",
booktitle = "Computer and Information Sciences - ISCIS 2005 - 20th International Symposium, Proceedings",
note = "20th International Symposium on Computer and Information Sciences, ISCIS 2005 ; Conference date: 26-10-2005 Through 28-10-2005",
}