Gender Identification Using Multilayer Perceptron (MLP) Neural Network, Genetic Neural Network and Adaptive Neuro-Fuzzy Inference System (ANFIS) Network - Evanthia Kokkinaki (Journal of Artificial Intelligence and System Modelling)
This study introduces and evaluates novel concepts and techniques for automatic gender identification. Several approaches may be identified, including the utilization of a multilayer perceptron neural network in conjunction with the adaptive neuro-fuzzy inference system algorithm and genetic algorithms to enhance the optimization of network weights. Additionally, the application of neural networks for gender recognition and their integration with the fuzzy C-means method is also noteworthy. The most optimal outcome was achieved through the integration of the ANFIS network with the Fuzzy C-Means algorithm. Furthermore, alternative approaches proved to be highly efficacious. The highest achieved accuracy was observed for the Texas Instruments/Massachusetts Institute of technology dataset (97.5%) and for the Oregon Graduate Institute dataset (96.31%). The demonstrated high accuracy achieved in the analysis of Oregon Graduate Institute data, characterized by its multilingual phone data and low signal-to-noise ratio, indicates the robustness of the suggested approaches in handling variations in speaker language and the suboptimal quality of speech data. Furthermore, employing the genetic neural network methodology facilitated the development of a high-speed network capable of attaining comparable levels of accuracy to the multilayer perceptron neural network, albeit with a significantly reduced number of neurons in the middle layer, expressly limited to three.