Evaluating the usage of short-time energy on voice biometrics system for cerebral palsy

This study was performed to evaluate the feasibility of short-time energy as an input vector features that will be used as a key of recognition in the voice biometric system to recognize the Cerebral Palsy (CP). To retrieve the characteristics of the voice, Mel-Frequencies Cepstral Coefficients (MFCC) was used as feature extraction algorithm, while Neuro Fuzzy was used as the classifier algorithm. The test results have shown that the system's ability to recognize people with CP based on their voice reached 100% with the accuracy rate at 91.18%. This rate shows that short-time energy deserves to be used as a feature for voice biometrics system, especially for CP.