Abstract
Changing the voice characteristics of a person under the influence of alcohol intoxication affects the effectiveness of the identification procedure. From which it follows that the determination of the level of alcohol in the blood remotely is important for ensuring information security in computer systems. The purpose of the present paper is to analyze the existing voice authentication methods and approaches to improve accuracy, analyze existing data sets, and evaluate the influence of the psychophysiological state of the speaker on the parameters of his speech. The focus of this study is on exploring different approaches to recognizing alcohol intoxication and evaluating their effectiveness. The study includes a comparative analysis of these methods, emphasizing the importance of using a comprehensive and representative data set that takes into account factors such as gender, age, stage of intoxication, recording quality, and ambient noise levels. Based on the analysis of scientific publications, the study identifies the random forest method as one of the most effective machine learning methods, demonstrating an accuracy of 95.3% for all speech datasets and 80% for the widely used Alcohol Language Corpus.
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