A Dual-Staged heterogeneous stacked ensemble model for gender recognition using speech signal. (30th March 2023) - kala, Jaideep; Taran, Sachin; Pandey, Anukul
Highlights: A dual-staged heterogeneous stacked ensemble model is introduced. A variance-based feature selection provides the lower computational complexity model. Meta-learner-controlled the over-fitting and improved the predictive accuracy. The proposed technique obtaining better performance for gender recognition. 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.