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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Сравнение механизмов фонематического восприятия и внутреннего проговаривания фонем и слогов: ЭЭГ- и фМРТ-исследование

Российский психологический журнал. 2022. Т. 19. № 4. С. 186–210.
Шевченко А. О., Вартанов А. В.

Introduction. The existing speech production schemes do not fully reflect the interaction of speech perception systems and one’s own internal speech. The study is aimed at comparing the connection between internal pronunciation and phonemic perception using psychophysi-ological methods. Methods. Twenty-five people took part in the EEG study. The subjects were offered auditory stimuli, and then it was necessary to internally pronounce the given phonemes or syllables with the same intonation and pronunciation as in external speech. Functional analysis of variance was used to process the results. An fMRI study was also conducted, and 30 healthy right-handed subjects took part in it. The respondents were also offered auditory stimuli, and the background, listening to the material, and listening with subsequent internal pronouncing of a given stimulus was recorded. The results were processed using a program for statistical parametric mapping and then analyzed by group statistics applying a one-sample Student t-test. Results. During the EEG investigation, intervals of significant differences in the structure of the evoked potential of internal pronunciation and phonemic perception were found. During the fMRI study, we obtained data that indicate both the process of phonemic perception and intentional internal pronunciation. Discussion. Differences in brain structures activity during in-ternal pronunciation and perception were analyzed. Based on the data obtained by us and theoretical analysis results, a scheme of phonemic perception and internal pronunciation was proposed. This scheme represents not only the interaction of the processes of perception and speech generation but also shows the influence of articulations on the internal speech process

Research target: Psychology
Language: Russian
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Keywords: локализациявнутренняя речьneurolinguisticsнейролингвистикавызванные потенциалыlocalizationфонематическое восприятиефонемыspeech fMRIspeech EEG evoked potentialsinternal speech subvocalisation phoneme syllable phonemic perceptionфМРТ речиЭЭГ речи субвокализация слоги
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Added: October 2, 2025
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Relevance. The problem of identifying the cerebral mechanisms of internal speech (spoken words or even individual sounds — phonemes) based on the electrical activity of the brain is relevant and significant for a number of practical applications, in particular for improving the construction of brain-computer interfaces. Target. Electroencephalographic research is aimed at identifying the cerebral mechanisms of internal speech, taking into ...
Added: October 2, 2025
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