FIELD: medicine.
SUBSTANCE: present invention refers to the means and methods that provide automated execution of diagnostic procedures in terms of performing a pre-medical preliminary classifying multifactorial assessment of the possibility of a human auditory analyzer during mass preventive examinations of the population. A method for pre-medical preliminary classifying multifactorial assessment of the possibility of a human auditory analyzer during mass preventive examinations of the population is proposed, performed using a computing device connected to audio signal playback devices and containing stages in which: using a computing device, a primary test speech sequence (TSS) is formed, which is sentences consisting of the first number of words based on a matrix test; a noise competing sound is formed for primary TSS; the primary TSS is reproduced using audio signal playback devices made in the form of air and bone sound transmission headphones, while the TSS is reproduced simultaneously with the noise competing sound at the first signal-to-noise ratio using speech simulation based on a deep machine learning model; the user receives an oral response; performs automatic analysis of the user's oral response to TSS recognition by converting it into a text form and analyzing the correctness of the response using a machine learning model; moreover, on the basis of the performed analysis of the user's oral responses, a dynamic change in the complexity of the assessment is carried out, in which, according to the result of each automatic analysis, a change in the number of words in sentences forming the TSS and/or the signal/noise ratio of the reproduced signal is performed; an assessment of the possibility of the user's auditory analyzer is performed based on responses when reproducing a test speech sequence.
EFFECT: invention provides an automated pre-medical preliminary classifying assessment of the possibility of a human auditory analyzer during mass preventive examinations of the population.
6 cl, 14 dwg, 3 tbl
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Authors
Dates
2022-01-25—Published
2021-05-24—Filed