Comparative Study on the Performance of Mel-Frequency Cepstral Coefficients and Linear Prediction Cepstral Coefficients under different Speaker's Conditions

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%.

Provided by: International Journal of Computer Applications Topic: Enterprise Software Date Added: Mar 2014 Format: PDF

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