Metaheuristic Approaches to Enhance Voice-Based Gender Identification Using Machine Learning Methods

Nowadays, classification of a person’s gender by analyzing characteristics of their voice is generally called voice-based identification. This paper presents an investigation on systematic research of metaheuristic optimization algorithms regarding machine learning methods to predict voice-based gender identification performance. Furthermore, four types of machine learning methods—Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN)—are employed to predict voice-based gender identification. On the other hand, initially, the dataset is preprocessed using raw data and normalized with z-score and min–max normalization methods. Second, six different hyperparameter optimization approaches, including four metaheuristic optimization algorithms (Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Artificial Fish Swarm Algorithm (AFSA)), along with random search and Tree-structured Parzen Estimator (TPE), are used to optimize the hyperparameters of the machine learning methods. A rigorous 5 × 10-fold cross-validation strategy is implemented to ensure robust model evaluation and minimize overfitting. A comprehensive evaluation was conducted using 72 different model combinations, assessed through accuracy, precision, recall, and F1-score metrics. The statistical significance of performance differences among models was assessed through a paired t-test and ANOVA for multiple group comparisons. In addition, external validation was performed by introducing noise into the dataset to assess model robustness under real-world noisy conditions. The results proved that metaheuristic optimization significantly outperforms traditional manual hyperparameter tuning approaches. Therefore, the optimal model, combining min–max normalization with RF optimized via the PSO algorithm, achieved an accuracy of 98.68% and an F1-score of 0.9869, representing competitive performance relative to the existing literature. This study demonstrated valuable insights into metaheuristic optimization for voice-based gender identification and presented a deployable model for forensic science, biometric security, and human–computer interaction. The results revealed that metaheuristic optimization algorithms demonstrated superior performance compared to traditional hyperparameter tuning methods and significantly improved the accuracy of voice-based gender identification systems.