Authors :
Mia Mutiany; Iwa Ovyawan Herlistiono
Volume/Issue :
Volume 5 - 2020, Issue 10 - October
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://www.scribd.com/document/481252980/Gender-Detection-by-Voice-Using-Deep-Learning
Abstract :
The recognition of gender voices as an
important part of answering certain voices. To
distinguish gender from sound signals, sound techniques
have defined the gender-relevant features (male or
female) of these sound signals. In this study, we used
various models to improve accuracy, one of which was
by using deep learning with the voice gender DNN
method. This noise reduction uses the extraction feature
of the Mel Frequency Cepstral Coefficient (MFCC), then
the sound classification uses SVM. By using a separation
ratio of 80% for training data and 20% for testing data.
The results showed that using DNN for voice recognition
was better and pairing with the SVM algorithm obtained
an accurate result of 0.97%.
Keywords :
Voice Recognition, Deep Neural Network, Deep Learning, MFCC, SVM.
The recognition of gender voices as an
important part of answering certain voices. To
distinguish gender from sound signals, sound techniques
have defined the gender-relevant features (male or
female) of these sound signals. In this study, we used
various models to improve accuracy, one of which was
by using deep learning with the voice gender DNN
method. This noise reduction uses the extraction feature
of the Mel Frequency Cepstral Coefficient (MFCC), then
the sound classification uses SVM. By using a separation
ratio of 80% for training data and 20% for testing data.
The results showed that using DNN for voice recognition
was better and pairing with the SVM algorithm obtained
an accurate result of 0.97%.
Keywords :
Voice Recognition, Deep Neural Network, Deep Learning, MFCC, SVM.
