| dc.description.abstract | Voice detection is very important for the world of information technology that can be
used for voice processing, biometrics, human computer interfaces. Voice identification carried out
in this study is based on speech or dialect using a prototype that has been designed using the
Raspiberry Pi device and other supporting devices. In its application, the regional
identification prototype uses sound feature extraction, namely Mel Frequency Cepstral
Coefficients (MFCC) and uses an artificial neural network method with a multilayer perceptron
(secos) developing algorithm. The purpose of this study is to identify regional origins based on
dialect or speech using the Mel Frequency Cepstral Coefficients (MFCC) extraction technique and
the Evolving Multilayer Perceptron method. The results of the regional recognition test produce
a good level of accuracy, with testing as an example of the aceh area with test data of 10 voice
samples, the results obtained by the prototype can identify voices with a success rate
of being able to recognize 7 voices out of 10 samples tested in the aceh region. From all the
tests on the
areas of Aceh, Karo, Nias, Simalungun, the accuracy was 88%. | en_US |
