The voices of different people are tested for 20 properties. These properties include mean-frequency, standard deviation, kurtosis, skew, mode frequency , modulation index , fundamental freq....,etc . My work includes the demonstation of the much probable properties showcased by females as well as males. The study of important attributes for voice recognition and their varied concentration in each gender using the inferences drawn from the various regression plots, pair plots, scatter plots , etc. Dataset is also standardized or normalized prior to training for better performance. Different models are tried . Also plotted their accuracy curves to understand the variation of parameters wrt accuracy. The parameters were tuned using repetitive piecewise gridsearch to compute things efficiently wrt time. Support Vector Machines are taken much care off till end and gave a cross-validated accuracy of 97.004 %. Further a train test spilt accuracy of 99.36 % given by XGBoost Classifier.
The voices of different people are tested for 20 properties. These properties include mean-frequency, standard deviation, kurtosis, skew, mode frequency , modulation index , fundamental freq....,etc . My work includes the demonstation of the much probable properties showcased by females as well as males. The study of important attributes for voice recognition and their varied concentration in each gender using the inferences drawn from the various regression plots, pair plots, scatter plots , etc. Dataset is also standardized or normalized prior to training for better performance. Different models are tried . Also plotted their accuracy curves to understand the variation of parameters wrt accuracy. The parameters were tuned using repetitive piecewise gridsearch to compute things efficiently wrt time. Support Vector Machines are taken much care off till end and gave a cross-validated accuracy of 97.004 %. Further a train test spilt accuracy of 99.36 % given by XGBoost Classifier.