A Dual-Staged heterogeneous stacked ensemble model for gender recognition using speech signal

Abstract

Gender classification is one of the most popular topics in the field of machine learning. It can be done with images or sounds. With the aim of developing a low-complexity and highly accurate gender recognition model, this paper proposes a Dual-staged Heterogeneous Stacked Ensemble Model (DH-SEM). In the proposed ensemble model, the classification problem is addressed by the amalgamation of three supervised base learners at stage-1 and random forest as a Meta learner in stage-2. The proposed work uses a speech dataset compiled from various sources (Harvard-Haskins, Carnegie Mellon and McGill University) and consists of statistical features. Rigorous feature engineering has been performed to select the most discriminative information from the dataset. For gender recognition, the DH-SEM method adopts the reduced feature space. The gender recognition accuracy of the proposed DH-SEM model is 99.36%, which is highest as compared to the state-of-the-art methods. The robustness of the proposed technique is also validated by other performance evaluation metrics and receiver operating characteristics.