In the paper, personalized results are presented in the methodology for monitoring information security based on voice authentication.Integration of sound preprocessing and Machine Learning techniques for feature extraction, training, and validation of classification models has been implemented.The objects of Diversified Ownership Structure and Dividend Pay‑outs of Publicly Traded Companies research are staked mixed-test voice profiles.Classifies were selected with quantitative evaluation under a threshold of 90.
00% by Naive Bayes and Discriminant Analysis.Significantly improved accuracy to approximate levels of 96.0% was established at Decision Tree synthesis.Strongly satisfactory performance indices were reached Bacterial assessment of food handlers in Sari City, Mazandaran Province, north of Iran at the diagnosis of voice profiles using Feed-Forward and Probabilistic Neural Networks, respectively, 98.
00% and 100.00%.