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The voice of each speaker has a unique specific character, influenced by gender, age, emotion, dialect, etc. The use of voice-based gender identification is growing rapidly, such as in the fields of security systems, speech recognition, artificial intelligence, etc. However, in speech processing, there are difficulties where the characteristics of the speech signal based on increasing age are difficult to determine accuracy, and there are overlapping fundamental frequency values between males and females. In this research, modeling of a gender identification system based on voice in real-time has been carried out on a Raspberry Pi device. This system is implemented by 2 methods, namely the YIN algorithm and feature extraction of Mel-Frequency Cepstral Coefficient (MFCC). The test results showed that the success of identification in the tuning parameters of scheme two is better than the first scheme by narrowing the overlapping frequency parameters. In the female test data in the closed test, the accuracy is from 98% to 100%, then in the open test starts from 92% to 96%. Meanwhile, the test data for the male closed test increased from 92% to 98%, and the open test started at 90% and rose to 94%. It indicates that the data used in this research is more suitable to use the second scheme parameter tuning to increase the accuracy of the results.


real-time gender YIN MFCC Raspberry Pi real-time gender YIN MFCC Raspberry Pi

Article Details

Author Biographies

Mirza Ardiana, Politeknik Elektronika Negeri Surabaya

Teknik Elektro Politeknik Elektronika Negeri Surabaya

Titon Dutono, Politeknik Elektronika Negeri Surabaya

Teknik Telekomunikasi Politeknik Elektronika Negeri Surabaya

Tri Budi Santoso, Politeknik Elektronika Negeri Surabaya

Teknik Telekomunikasi Politeknik Elektronika Negeri Surabaya
How to Cite
Ardiana, M., Dutono, T., & Budi Santoso, T. (2022). Real-time Gender Identification Using Voice On Raspberry Pi. Jurnal Komputer Terapan, 8(1), 158–167.


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