Using PCA to Improve the Generation of Speech Keys

  • Juan A. Nolazco-Flores
  • J. Carlos Mex-Perera
  • L. Paola Garcia-Perera
  • Brenda Sanchez-Torres
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)


This research shows the improvement obtained by including the principal component analysis as part of the feature production in the generation of a speech key. The main architecture includes an automatic segmentation of speech and a classifier. The first one, by using a forced alignment configuration, computes a set of primary features, obtains a phonetic acoustic model, and finds the beginnings and ends of the phones in each utterance. The primary features are then transformed according to both the phone model parameters and the phones segments per utterance. Before feeding these processed features to the classifier, the principal component analysis algorithm is applied to the data and a new set of secondary features is built. Then a support vector machine classifier generates an hyperplane that is capable to produce a phone key. Finally, by performing a phone spotting technique, the key is hardened. In this research the results for 10, 20 and 30 users are given using the YOHO database. 90% accuracy.


Principal Component Analysis Support Vector Machine Radial Basis Function Speech Signal Support Vector Machine Model 
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

  • Juan A. Nolazco-Flores
    • 1
  • J. Carlos Mex-Perera
    • 1
  • L. Paola Garcia-Perera
    • 1
  • Brenda Sanchez-Torres
    • 1
  1. 1.Computer Science DepartmentITESMMéxico

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