  Human voice is the source of several important information. This is in the
form of features. These Features help in interpreting various features
associated with the speaker and speech. The speaker dependent work
researchersare targeted towards speaker identification, Speaker verification,
speaker biometric, forensics using feature, and cross-modal matching via speech
and face images. In such context research, it is a very difficult task to come
across clean, and well annotated publicly available speech corpus as data set.
Acquiring volunteers to generate such dataset is also very expensive, not to
mention the enormous amount of effort and time researchers spend to gather such
data. The present paper work, a Neural Network proposal as NeuraGen focused
which is a low-resource ANN architecture. The proposed tool used to classify
gender of the speaker from the speech recordings. We have used speech
recordings collected from the ELSDSR and limited TIMIT datasets, from which we
extracted 8 speech features, which were pre-processed and then fed into
NeuraGen to identify the gender. NeuraGen has successfully achieved accuracy of
90.7407% and F1 score of 91.227% in train and 20-fold cross validation dataset.
