Voice-Based Gender Identification Using Machine Learning

Gender identification is considered to be one of the major problems in the field of signal processing. Formerly, this problem has been solved using various image classification techniques which typically includes information extraction from a set of images. However, gender classification using vocal features has recently been a topic of interest to a lot of researchers across the globe. A close scrutiny of some of the human vocal features reveals that classifying gender goes way beyond just the frequency and the pitch of a person. One of the most challenging problems faced in machine learning is feature selection or as is technically known as dimensionality reduction. A similar problem is faced while deciding gender-specific traits-which serve a significant purpose in classifying the gender of a person. This paper will inspect the efficiency and significance of machine learning algorithms to the voice-based gender identification problem.