Simple Noise Robust Feature Vector Selection Method for Speaker Recognition

  • Gabriel Hernández
  • José R. Calvo
  • Flavio J. Reyes
  • Rafael Fernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

The effect of additive noise in a speaker recognition system is known to be a crucial problem in real life applications. In a speaker recognition system, if the test utterance is corrupted by any type of noise, the performance of the system notoriously degrades. The use of a feature vector selection to determine which speech frames are less affected by noise is the purpose in this work. The selection is implemented using the euclidean distance between the Mel features vectors. Results reflect better performance of robust speaker recognition based on selected feature vector, as opposed to unselected ones, in front of additive noise.

Keywords

speaker verification cepstral features selected feature vector channel mismatch 

References

  1. 1.
    Ming, J., Hazen Timothy, J., Glass James, R., Reynolds Douglas, A.: Robust Speaker Recognition in Noisy Conditions. IEEE Trans. on ASLP 15(5) (July 2007)Google Scholar
  2. 2.
    Reynolds, D.A.: Channel robust speaker verication via feature mapping. Proc. of ICASSP, pp. II-53-6 (2003)Google Scholar
  3. 3.
    Teunen, R., Shahshahani, B., Heck, L.: A model-based transformational approach to robust speaker recognition. In: Proc. of ICSLP (2000)Google Scholar
  4. 4.
    Fauve, B.G.B., Matrouf, D., Scheffer, N., Bonastre, J.-F., Mason, J.S.D.: State-of-the-Art Performance in Text-Independent Speaker Verification Through Open-Source Software. IEEE Trans. on ASLP 15(7), 1960–1968 (2007)Google Scholar
  5. 5.
    Douglas, A., Richard, R.y., Rose, C.: Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models. IEEE Trans. on SAP 3(1) (January 1995)Google Scholar
  6. 6.
    Ortega-Garcia, J., Gonzalez-Rodriguez, J., Marrero-Aguiar, V.: AHUMADA A large speech corpus in Spanish for speaker characterization and identification. Speech communication (31), 255–264 (2000)CrossRefGoogle Scholar
  7. 7.
    Martin, A., et al.: The DET curve assessment of detection task performance. Proc. of EuroSpeech 4, 1895–1898 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Gabriel Hernández
    • 1
  • José R. Calvo
    • 1
  • Flavio J. Reyes
    • 1
  • Rafael Fernández
    • 1
  1. 1.Advanced Technologies Application Center 

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