Performance Evaluation of PBDP Based Real-Time Speaker Identification System with Normal MFCC vs MFCC of LP Residual Features

Present study compares, Mel Frequency Cepstral Coefficients (MFCC) of Linear Predictive (LP) Residuals with normal MFCC features using both VQ and GMM based speaker modeling approaches for performance evaluation of real- time Automatic Speaker Identification systems including both co-operative and non co-operative speaking scenario. Pitch Based Dynamic Pruning (PBDP) technique is applied regarding optimization of Speaker Identification process. System is trained and tested with voice samples of 62 speakers across different age groups. Residual of a signal contains information mostly about the source, which is speaker specific. Result shows that, in co-operative speaking, MFCC of LP residuals outperform normal MFCC features for both VQ and GMM based speaker modeling with an improvement of 7.6% and 6.8% in average accuracy respectively. But combined modeling of both features (source and vocal tract) is required for non co-operative speaking in real-time as it enhances the highest identification accuracy from 67.7% to 83%.