Ensemble Learning Model for Gender Recognition Using the Human Voice

What is it about?

Gender recognition through the human voice is a rapidly growing field of research. It is a vital research subject as the human voice now has an ever-growing list of applications, especially for social good. Gender recognition using voice is beneficial in multiple applications such as online healthcare systems, interactive services, crime analysis, security systems, etc. Algorithms have already been deployed, but accuracy and efficiency can still be improved. This work aims to classify human gender based on human voice data between males and females. It uses modified ensemble techniques based on classifiers such as k-NN (k-nearest neighbors), Random Forest (RF), and SVM (support vector machine). In this study, a benchmark dataset is used which includes 3168 instances and 21 attributes, where 20 attributes are the predictors, and one attribute is the target – ‘male’ or ‘female’ as instances. To evaluate the proposed model’s results, precision, accuracy, recall, and F1-score were calculated. When compared to traditional machine learning models employed independently, the ensemble model imposed better results, with an accuracy of 99.05%.

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Why is it important?

The importance of gender recognition through voice analysis lies in the growing number of applications that benefit from this technology, particularly for social benefit. This research is important because it contributes to the development of more accurate and efficient gender recognition systems. These systems can be used to improve the performance of speaker recognition systems, enhance human-computer interaction, and provide a more personalized user experience.

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