Gender Detection by Acoustic Characteristics of Sound with Machine Learning Algorithms
Year 2023,
Volume: 4 Issue: 1, 24 - 28, 30.06.2023
Hamit Mızrak
,
Serpil Aslan
Abstract
Sound has been studied in almost every field since the existence of humans, and sound science branches have emerged due to its increasing importance day by day. Sound has been studied in many fields from the past to the present, and it has become an essential factor in people's understanding of each other. It is possible to determine the voice of individuals with the help of computers in the digitalized world to determine the speech acoustics and the gender of that voice and to determine it with the prediction algorithms in machine learning by utilizing the characteristics of the voice's characteristic metric performance criteria. In this study, six different machine learning algorithms were used. By comparing these machine learning algorithms, prediction results were obtained to determine the best prediction result and the gender difference by using the voice characteristics of individuals.
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Year 2023,
Volume: 4 Issue: 1, 24 - 28, 30.06.2023
Hamit Mızrak
,
Serpil Aslan
References
- Chaudhari, S. J., & Kagalkar, R. M. (2015). Automatic speaker age estimation and gender dependent emotion recognition. International Journal of Computer Applications, 117(17), 5-10.
- Eckert, M. A., Matthews, L. J., & Dubno, J. R. (2017). Self-assessed hearing handicap in older adults with poorer-than-predicted speech recognition in noise. Journal of Speech, Language, and Hearing Research, 60(1), 251-262.
- Ranjan, R., Sankaranarayanan, S., Castillo, C. D., & Chellappa, R. (2017, May). An all-in-one convolutional neural network for face analysis. In 2017 12th IEEE international conference on automatic face & gesture recognition (FG 2017) (pp. 17-24). IEEE.
- Gamit, M. R., Dhameliya, K., & Bhatt, N. S. (2015). Classification techniques for speech recognition: a review. International Journal of Emerging Technology and Advanced Engineering, 5(2), 58-63.
- Karasulu, B., Yücalar, F., & Borandağ, E. (2022). A hybrid approach based on deep learning for gender recognition using human ear images. Journal of the Faculty of Engineering and Architecture of Gazi University, 37(3), 1579-1594.
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- Kabil, S. H., Muckenhirn, H., & Magimai-Doss, M. (2018, September). On Learning to Identify Genders from Raw Speech Signal Using CNNs. In Interspeech, 287, 291.
- Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4), 18-28.
- Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27.
- Michie, D., Spiegelhalter, D. J., Taylor, C. C., & Campbell, J. (Eds.). (1995). Machine learning, neural and statistical classification. Ellis Horwood.