Abstract
Abstract: A study on the detection of gender, based on voice using artificial neural networks. In today’s fast-moving world Gender classification through voice plays an important role to enhance performance in Speech recognition systems, Forensic investigation, and Marketing. The dataset has 3,168 recorded voice samples of male and female voices. The samples are produced by using acoustic analysis. The primary goal of the proposed model is to automate the system to identify gender based on the audio signals and to test the voice of a human on the spot. Multilayer Perceptron (MLP) with ReLU activation function as a model has been trained to predict gender. Nadam optimizer is used for the optimization of neural networks, K-Nearest Neighbor and Support Vector Machines are trained on the dataset of 3,168 records. The obtained best accuracy is 97% on the given dataset by MLP algorithm. An Interactive web page has been built to test the voice without interruption and to predict its gender. 125 real time samples are tested out of which model could classify every record into male/female. 19 male records are incorrectly classified.
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