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01-Applied Mathematics & Information Sciences
An International Journal
               
 
 
 
 
 
 
 
 
 
 
 
 

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Volumes > Volume 07 > No. 4

 
   

Robust FHPD Features from Speech Harmonic Analysis for Speaker Identification

PP: 1591-1598
Author(s)
Shuiping Wang, Zhenmin Tang, Ye Jiang, Ying Chen,
Abstract
Speaker identification accuracy decreases significantly in the presence of additive noise. In this paper, we propose a robust speech feature extraction method, which is based on the harmonic structure of voiced segments. The robust features are composed of fundamental and harmonic peak data from short-time spectrum. These features are evaluated by thirty speaker data from TIMIT database and additive noise signals from NOISEX-92 database with clean training and noisy testing samples. Results reflect that under low SNR (signal-to-noise ratio) environments new features achieve better performance than conventionalMFCC (Mel-Frequency Cepstral Coefficients) parameters.

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