Speaker recognition in unknown mismatched conditions using augmented PCA

Authors:
Ha-Jin Yu
Affiliations:
School of Computer Science, University of Seoul, Dongdaemungu, Seoul, South Korea
Venue:
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Year:
2005

Citing 2
Cited 1

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Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Acoustical and Environmental Robustness in Automatic Speech Recognition

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Hybrid algorithm applied to feature selection for speaker authentication

MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence

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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%.