| dc.description.abstract | Voice signal can be used to represent a speaker. Voice can also be used to recognize the age range
and the gender of the speaker, because of the difference in frequency of the voice signal for
different age and gender. Age range for this experiment is divided into children (8-10 years old),
teenagers (12-17 years old) and adults (30-50 years old). We performed feature extraction to
obtain a representation of the speaker using Mel-Frequency Cepstrum Coefficient (MFCC). MFCC
is used with 13, 20, 26 coefficients. Probabilistic Neural Network (PNN) is used as the feature
matching model. The PNN model is constructed from three different proportions of TRD (Training
data) : TSD (Testing data) (25%:75%, 50%:50%, 75%:25%). MFCC with 26 coefficient gives the
best accuracy of 93.47%. The proportion of 75% (TRD) : 25% (TSD) also gives the best accuracy
of 94.22%. PNN-age and PNN-gender identification result in an accuracy of 91.26%. Specifically,
PNN accuracy percentage for male and female are 92.26%, and 95.20%, respectively, while for
children, teenagers and adults the accuracies are 99.85%, 92.28% and 95.57%, respectively. In
conclusion, the average of accuracy for all categories is 91.26%. The experiment using the voice
of male-teenagers gives the worst accuracy, which is caused by the overlapping frequencies of
voice in children, teenagers and adults. | id |
