Gender Voice Recognition with Classification approach using Random Forest and Decision Tree Algorithms
Mukesh Tadi, S Prasad Babu Vagolu, Sunil Chandolu · International Journal of Research in Advent Technology · 2020
Gender identification is one of the major problems of the speech processing.Gender tracking from aural data like median, frequency, and pitch.Machine learning provides auspicious results for the problem of classification in all domains.There are a few standards to work on to appraise the algorithms.Our model comparisons algorithm for appraising different learning algorithms is based on different metrics for classifying gender and aural data.An important parameter in evaluating any algorithms is their performance.The degree of variability should be low for classification set of problems; means the accuracy rate should be pretty high.The position and gender of the person became pretty important in financial markets by the form of AdSense.With this model comparisons algorithm, we tried different ML algorithms and came up with the best fit for the gender classification of aural data.