Voice Biometrics for Indonesian Language Users using Algorithm of Deep Learning CNN Residual and Hybrid of DWT-MFCC Extraction Features

This research develops a Voice Biometrics model for the Indonesian language users by using deep learning algorithm of CNN Residual and Hybrid of DWT-MFCC Feature Extraction. The voice dataset of Indonesian speakers were created with a duration of 5, 10, 15, 20, and 25 minutes. The testing phase of speaker recognition and speech recognition were carried out by comparing the model of CNN Residual with CNN Standard. In the phase of speaker recognition, CNN Residual model has obtained the best results with the highest precision percentage of 99.91% and the highest accuracy of 99.47% at 25 minutes voice samples, compared to the CNN Standard obtaining precision of 96.83% and accuracy of 99.00%. In the phase of speech recognition, CNN Residual model has reached the best performance at 100% accuracy during 20 trials, while CNN Standard only gave 95% accuracy. CNN Residual Model provides a better performance for its accuracy and precision, but it is slightly slower than the CNN Standard, with a time difference of 0.03 – 1.28 seconds.