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This paper compares Mel-Frequency Cepstral Coefficients (MFCCs) and Linear Prediction Cepstral Coefficients (LPCCs) features under three speaker conditions: waking up, being fully awake and being tired, to determine which is better at handling the effect of these variations. A Gaussian Mixture Model (GMM) Classifier was used for both features. Experimental results show an identification rate of 83.3% in the MFCC based system when the speakers were just waking up, while the LPCC based system had a lower identification rate of 75%. Also, when the speakers were either fully awake or tired, the MFCC based system achieved an identification rate of 100%, while the LPCC based system had an Identification rate of 91.7%. In speaker verification, under the first condition (Waking Up), there is a significant difference between the equal error rates (EER), 7.9% for MFCC and 22.0% for LPCC. Also, there is a significant difference between the total success rates (TSR) under this condition. 82.5% for MFCC and 65.0% for LPCC. Overall, MFCC achieved a better total success rate under the three conditions studied. General Terms Speaker Recognition, intra-speaker variability, session variability.

international journal of computer applications

Comparative Study on the Performance of Mel-Frequency Cepstral Coefficients and Linear Prediction Cepstral Coefficients under different Speaker&amp;apos;s Conditions