Survey and evaluation of acoustic features for speaker recognition

This study seeks to quantify the effectiveness of a broad range of acoustic features for speaker identification and their impact in feature fusion. Sixteen different acoustic features are evaluated under nine different acoustic, channel and speaking style conditions. Three major types of features are examined: traditional (MFCC, PLP, LPCC, etc.), innovative (PYKFEC, MVDR, etc.) and extensions of these (frequency-constrained LPCC, LFCC). All features were then fused in binary and three-way fusion to determine the complementarity between features and their impact on accuracy. Results were surprising, with the MVDR feature having the highest performance for any single feature, and LPCC based features having the greatest impact on fusion effectiveness. Commonly used features like PLP and MFCC did not achieve the best results in any category. It was further found that removing the perceptually-motivated warping from MFCC, MVDR and PYKFEC improved the performance of these features significantly.