Voice based automatic person identification system using Vector Quantization

This paper presents the design, implementation, and evaluation of a research work for developing an automatic person identification system using voice biometric. The developed automatic person identification system mainly used toolboxes provided by MATLAB environment. To extract features from voice signals, Mel-Frequency Cepstral Coefficients (MFCC) technique was applied producing a set of feature vectors. Subsequently, the system uses the Vector Quantization (VQ) for features training and classification. In order to train and test the developed automatic person identification system, an in-house voice database is created, which contains recordings of 100 persons´ usernames (50 males and 50 females) each of which is repeated 30 times. Therefore, a total of 3000 utterances are collected. This paper also investigates the effect of the persons´ gender on the overall performance of the system. The voice data collected from female persons outperformed those collected from the male persons, whereby the system obtained average recognition rates of 94.20% and 91.00% for female and male persons, respectively. Overall, the voice based system obtained an average recognition rate of 92.60% for all persons.