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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.
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Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Vol. 6. International Speech and Communication Association, 2021.
Editor-in-chief: H. Heřmanský, H. Černocký
Chapters
Ensemble-within-ensemble classification for escalation prediction from speech
Verkholyak O., Dresvyanskiy D., Dvoynikova A. et al., , in: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECHVol. 6.: International Speech and Communication Association, 2021. P. 4321–4325.
Conflict situations arise frequently in our daily life and often require timely response to resolve the issues. In order to automatically classify conflict (also referred to as escalation) speech utterances we propose ensemble learning as it improves prediction performance by combining several heterogeneous models that compensate for each other’s weaknesses. However, the effectiveness of the ...
Added: April 24, 2026
Language: English
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Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
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