Javanese Gender Speech Recognition Based on Machine Learning Using Random Forest and Neural Network

Abstract

Speech is a means of communication between people throughout the world. At present research in the field of speech recognition continues to develop in producing a robust method in various research variants. However decreasing the word error rate or reducing noise is still a problem that is still being investigated until now. The purpose of this study is to find the right method with high accuracy to classify the gender voices of Javanese. This research used a human voice dataset of both men and women from the Javanese tribe which was recorded and then processed using a noise reduction preprocessing technique with the MFCC extraction feature method and then classified using 2 machine learning methods, namely Random Forest and Neural Network. Evaluation results indicate that the classification of Javanese accent speech accents results in an accuracy rate of 91.3 % using Random Forest and 9 2 . 2 % using Neural Network.