The impressive improvement in performance obtained using neural networks for automatic speech recognition (ASR) have motivated the application of neural networks to other speech technologies such as speaker, emotion, language, and gender recognition. Prior work has shown significant improvement in gender recognition from images and videos. This paper uses speech to build a gender recognition system based on neural networks. Three types of neural networks are investigated to find the best model for gender recognition system using Yorùbá, namely, feed-forward artificial neural networks (Multilayer Perceptrons), Recurrent neural networks (long short-term memory), and Convolutional neural networks. All the classifier models obtained the state-of-the-art performance in speech-based gender recognition with 99% in accuracy and F1 score.
Reference:
Sefara, T.J. & Modupe, A. 2019. Yorùbá Gender Recognition from Speech using Neural Networks. In: 2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI 2019), Johannesburg, South Africa, 19-20 November 2019, pp. 50-55
Sefara, T. J., & Modupe, A. (2019). Yorùbá Gender Recognition from Speech using Neural Networks. IEEE. http://hdl.handle.net/10204/11530
Sefara, Tshephisho J, and Abiodun Modupe. "Yorùbá Gender Recognition from Speech using Neural Networks." (2019): http://hdl.handle.net/10204/11530
Sefara TJ, Modupe A, Yorùbá Gender Recognition from Speech using Neural Networks; IEEE; 2019. http://hdl.handle.net/10204/11530 .
Presented in: 2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI 2019), Johannesburg, South Africa, 19-20 November 2019. Due to copyright restrictions, the attached PDF file contains the accepted version of the published paper. For access to the full-text item, please consult the publisher's website.
