Due to its widespread application in a variety
of circumstances, the gender classification system has
become more and more relevant including social media
platforms and criminal investigations. Prior studies in
this area have mostly focused on discrimination against
both men and women. Nevertheless, since transgender
persons have just received legal recognition, it has been
vital to create techniques for accurately diagnosing
gender from a specific voice, which can be a challenging
undertaking. To extract pertinent characteristics from a
training set that may be utilised to create a model for
gender categorization, researchers have employed a
number of techniques. Following then, a vocal signal’s
gender may be ascertained using this model. The study
makes three significant contributions: first, it provides a
thorough analysis of well-known voice signal features
using a well-known dataset; second, it investigates a
variety of machine learning models from a variety of
theoretical families to classify voice gender; and third,
it uses three well-known feature selection algorithms to
select the features that have the greatest potential to
improve classification models.
Keywords :
Python, Machine Learning, Transformer, Ten- Sorflow, Spectrogram, Matplotlib, Pandas, HTML, CSS, Django.