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.
📌 3 Key Takeaways
🎯
Objective & Hypothesis: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.
📊
Key Findings & Data: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.
💡
Conclusion & Impact: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.
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.
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.
Mysak, E. D. (1959). Pitch and duration characteristics of older males. Journal of Speech and Hearing Research, 2(1), 46-54.
Maka, T., & Dziurzanski, P. (2014, April). An analysis of the influence of acoustical adverse conditions on speaker gender identification. In XXII Annual Pacific Voice Conference (PVC) (pp. 1-4).
IEEE. Sedaghi, M. (2009). A comparative study of gender and age classification in speech signals. Iranian Journal of Electrical & Electronic Engineering, 5(1), 1-12.
Pahwa, A., & Aggarwal, G. (2016). Speech feature extraction for gender recognition. International Journal of Image, Graphics and Signal Processing, 8(9), 17. La Mura, M., & Lamberti, P. (2020, June). Human-machine interaction personalization: a review on gender and emotion recognition through speech analysis. In 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT (pp. 319-323).
IEEE. Lee, M. W., & Kwak, K. C. (2012). Performance comparison of gender and age group recognition for human-robot interaction. International Journal of Advanced Computer Science and Applications, 3(12), 207-211.
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(