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"Voice Breathalyzer"
A program capable of assessing the degree of alcohol intoxication of a smartphone user using a “voice print”
Purpose and operating principles

The program is supposed to use ensembles of deep neural networks pre-trained on a large sample of speakers who were in various stages of alcohol intoxication.

A person pronounces a key phrase several times or reproduces an arbitrary fragment of speech, and the mobile application must recognize the stage of alcohol intoxication in which the person is supposed to be, in accordance with one of the well-known classifications, for example, the US Federal Flight Regulations (91.17: Alcohol and Piloting) or methodological instructions of the Ministry of Health (dated 07/03/1974 “On the forensic medical diagnosis of fatal ethyl alcohol poisoning and errors made in this case”). In this case, an interval estimate of the blood alcohol content (in ppm) should be displayed.

The application is developed for the most popular mobile operating systems.

OS Android

Key publications
on the topic "Analysis and recognition of speaker's voice images"
Highly reliable two-factor biometric authentication based on handwritten and voice passwords using flexible neural networks
hybrid networks, quadratic forms, Bayesian functionals, handwritten passwords,
voice parameters, wide neural networks, biometrics-code converters, protected neural containers.

Sulavko AE. Highly reliable two-factor biometric authentication based on handwritten

and voice passwords using flexible neural networks. Computer Optics 2020; 44(1): 82-91.

DOI: 10.18287/2412-6179-CO-567

The paper addresses a problem of highly reliable biometric authentication based on converters of secret biometric images into a long key or password, as well as their testing on relatively small samples (thousands of images). Static images are open, therefore with remote authentication they are of a limited trust. A process of calculating the biometric parameters of voice and handwritten passwords is described, a method for automatically generating a flexible hybrid network consisting of various types of neurons is proposed, and an absolutely stable algorithm for network learning using small samples of “Custom” (7-15 examples) is developed. A method of a trained hybrid "biometrics-code" converter based on knowledge extraction is proposed. Low values of FAR (false acceptance rate) are achieved
Influence of a Speaker’s Psycho-physiological State to His Voice Parameters and Results of Biometric Authentication by Speech Enabled Password
pattern recognition, speech signal parameters, speech enabled password, biometric authentication, psychophysiological state of the speaker, state of alcoholic intoxication.

Sulavko A.E., Eremenko A.V., Borisov R.V., Inivatov D.P. Influence of a Speaker’s Psycho-physiological State to His Voice Parameters and Results of Biometric Authentication by Speech Enabled Password // Computer instruments in education. - 2017. - №. 4. - pp. 29-47

In this work, two methods were used to calculate the identification characteristics of the speaker’s voice. One of them is based on the direct Fourier transform, the second — on the window transformation with the subsequent integration of the values of each harmonic of all the windows. The information content of these characteristics is determined. An estimation is given of how the parameters of the voice and their informativeness change depending on the degree of alcoholic intoxication of a person and in
a sleepy state. A computational experiment was carried out to evaluate the reliability of recognition of speakers in the space of selected features using functionals based on the Bayesian hypothesis formula, Pearson measure, chi-module measure, Gini criterion, Cramervon Mises, and perceptrons trained in GOST R 52633.5-2011, and networks of quadratic forms. An estimation is given of the stability of these methods and functionals to the psychophysiological state of the speaker in terms of the robustness of the obtained recognition results.
Generation of key sequences based on voice messages
voice messages, fuzzy extractor, noiseless coding, biometrics, speaker identification
get acquainted with the work on

Sulavko A.E., Eremenko A.V., Borisov R.V. Generation of cryptographic keys based on voice messages // Applied informatics / NOU VPO “MFPU “Synergy”, Moscow, 2016, No. 5, pp. 76-89

The problem of the generation of the key sequences on the basis of biometric data is described.
Objective: To develop a method of generating a key sequence based on the subject of voice parameters with indicators of reliability and key length exceeding achieved. Two features spaces of human voice are proposed: dependent and independent of the uttered phrase. The methods of generating keys based on voice messages on the basis of fuzzy extractors using Hadamard or Bose — Chaudhuri —Hocquenghem error correcting codes are proposed. Also the ranking procedure of most stable features individual for each subject was proposed. The effectiveness of the proposed method was defined. The optimum methods for each proposed feature space have been found. These results are superior to previously achieved by generating a key sequence based on voice.

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