VOICE BASED GENDER CLASSIFICATION USING MACHINE LEARNING
Author(s):
K. GEETHIKA ANASUYA, K. VENKATA RAMANA, D. SOUJANYA
Keywords:
Voice Recognition, Machine Learning, Random Forest classifier, Decision Tree Classification, K-Nearest Neighbor, Gaussian Naïve Bayes, Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Logistic Regression, ADA Boost, Guardian Boosting.
Abstract
Gender identification is one of the major problem speech analysis today. Discovering the gender from acoustic data i.e., pitch, median. Frequency etc. Machine learning gives ominous results for classification problem in all the research domains. There are several performance metrics to assess algorithms of an area. Aim is to identify gender, with five different algorithms: Linear Discriminant Analysis, K-Nearest Neighbor, Characterization and Regression Trees, Random Forest, and Support Vector Machine on premise of eight unique techniques. The main parameter in assessing any algorithms is its performance. Misclassification rate must be less than in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are using the different ML algorithms and find the best one for gender classification of acoustic data.
Article Details
Unique Paper ID: 153285

Publication Volume & Issue: Volume 8, Issue 6

Page(s): 312 - 317
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