Human Voice Recognition System to Predict the Gender using Random Forest Algorithm

There is an effective strategy in this research work to predict human voice recognition regardless of gender involving Random Forest in correlation with Novel Decision Tree calculation with further developed precision. Random Forest algorithm is an AI-based calculation which is a type of Recursive Element Disposal. The grouping precision of the Novel Decision Tree was further developed in the wake of applying the Random Forest to the dataset. Almost all the near calculations in characterization enabled orientation voice recognition to be a reasonable possibility. Resources and Procedure: The unique calculations with G-power levels of 80% with 10 research cases. In order to categorize the voice dataset into male or female, various properties are taken into account. Nearly 112 voice tests are included in the training dataset. Male or female voice recognition may be predicted with 97.79% accuracy according to research by Random Forest. Result: This examination concentrates on saw as 96.74% of accuracy for an expectation of human voice recognition utilizing the Novel Decision Tree algorithm with a measurably huge contrast among the double gatherings (
$\mathrm{p}=0.090;\ \mathrm{p} < 0.05$
) with a 95% assurance span. Conclude: These study explanations that the Random Forest algorithm scheduled the prediction of human voice response is fundamentally well associated to the novel decision tree algorithm.