Title :
Robust cepstral features for speaker identification
Author :
Assaleh, Khaled T. ; Mammone, Richard J.
Author_Institution :
Center for Comput. Aids for Ind. Productivity, Rutgers Univ., Piscataway, NJ, USA
Abstract :
In this paper we introduce a new set of features that provides improved performance for speaker identification. This feature set is referred to as the adaptive component weighting (ACW) cepstral coefficients. The ACW scheme modifies the linear predictive (LP) spectral components (resonances) so as to emphasize the formant structure by attenuating the broad-bandwidth spectral components. Such components are found to introduce undesired variability in the LP spectra of speech signals due to environmental factors. The ACW cepstral coefficients represent an adaptively weighted version of the LP cepstrum. The adaptation results in deemphasizing the irrelevant variations of the LP cepstral coefficients on a frame-by-frame basis. Experiments are presented using the San Diego portion of the King database. The ACW cepstrum is shown to offer improved speaker identification performance as compared to other common methods of cepstral weighting
Keywords :
cepstral analysis; prediction theory; speaker recognition; speech processing; King database; San Diego portion; adaptive component weighting; broad-bandwidth spectral components; cepstral coefficients; environmental factors; formant structure; frame-by-frame basis; linear predictive spectral components; performance; resonances; robust cepstral features; speaker identification; speech signals; Cepstral analysis; Cepstrum; Computer industry; Degradation; Environmental factors; Productivity; Robustness; Speaker recognition; Speech; Testing;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-1775-0
DOI :
10.1109/ICASSP.1994.389338
