Gender identification in Sepedi speech corpus

Abstract

Gender identification is the task of identifying the gender of the speaker from the audio signal. Most gender identification systems are developed using datasets belonging to well-resourced languages. There has been little focus on creating gender identification systems for under resourced African languages. This paper presents the development of a gender identification system using a Sepedi speech dataset containing a duration of 55.7 hours made of 30776 males and 28337 females. We build a gender identification system using machine learning models that are trained using multilayer Perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM). Mid-term features are extracted from time domain features, frequency domain features and cepstral domain features, and normalised using the Z-score normalisation technique. XGBoost is used as a feature selection method to select important features. MLP achieved the same F-score and an accuracy of 94% for data with seen speakers while LSTM and CNN achieved the same F-score and an accuracy of 97%. We further evaluated the models on data with unseen speakers. All the models achieved good performance in F-score and accuracy.