Robust Text-Independent Speaker Identification Using Hybrid PCA&LDA

  • Min-Seok Kim
  • Ha-Jin Yu
  • Keun-Chang Kwak
  • Su-Young Chi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)


We have been building a text-independent speaker recognition system in noisy conditions. In this paper, we propose a novel feature using hybrid PCA/LDA. The feature is created from the convectional MFCC(mel-frequency cepstral coefficients) by transforming them using a matrix. The matrix consists of some components from the PCA and LDA transformation matrices. We tested the new feature using Aurora project Database 2 which is intended for the evaluation of algorithms for front-end feature extraction algorithms in background noise. The proposed method outperformed in all noise types and noise levels. It reduced the relative recognition error by 63.6% than using the baseline feature when the SNR is 15dB.


Face Recognition Linear Discriminant Analysis Gaussian Mixture Model Principal Component Analysis Automatic Speech Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Min-Seok Kim
    • 1
  • Ha-Jin Yu
    • 1
  • Keun-Chang Kwak
    • 2
  • Su-Young Chi
    • 2
  1. 1.School of Computer ScienceUniversity of SeoulSeoulKorea
  2. 2.Human-Robot Interaction Research Team, Intelligent Robot Research DivisionElectronics and Telecommunication Research Institute (ETRI)Korea

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