Identification of Gender Based on Speech Signal
Andrzej Majkowski, Marcin Kołodziej, Jakub Pyszczak, Paweł Tarnowski, Remigiusz Jan Rak · 2019
The article presents a gender identification based on speech signal with supervised machine learning implementation. At first, a database of speech signals in Polish language was collected. Next, a set of features from audio signal were calculated. The features were farther used to train a neural network. Audio signal processing and implementation of the neural network were performed in Python, and the calculation of features in the R language. Neural network training process was carried out using only CPU, then CPU with GPU and the times of the programs execution were compared. The obtained accuracy of gender recognition was 92.4%. The use of GPU accelerated the network learning process several times.