Provided by: International Journal of Computer Applications
Topic: Enterprise Software
Date Added: Mar 2014
In this paper, the authors compare 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%.