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August 13, 2026
‘Working with AI Solves a Wide Range of Engineering Problems
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
August 12, 2026
‘I Would Like My Research to Help Make the World a Calmer and Better Place
Whatever task Saraa Ali, Junior Research Fellow at the Laboratory of Methods for Big Data Analysis (LAMBDA) of the AI and Digital Science Institute (HSE Faculty of Computer Science), is working on, she thinks about how it can benefit people. She told the Young Scientists of HSE University project about her large family, diagnosing three-phase motors, and her dream of building a children’s home in her native country.
August 11, 2026
‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.

 

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Audio-Visual Speech Recognition In-The-Wild: Multi-Angle Vehicle Cabin Corpus and Attention-Based Method

P. 8195–8199.
Axyonov Alexandr, Ryumin Dmitry, Ivanko D., Kashevnik A., Karpov A.

Audio-visual speech recognition (AVSR) gains increasing attention as an important part of human-machine interaction. However, the publicly available corpora are limited, particularly in driving conditions with prevalent background noise. Research so far has been collected in constrained environments, and thus cannot reflect the true performance of AVSR systems in real-world scenarios. Moreover, data for languages other than English is often unavailable. To meet the request for research on AVSR in unconstrained driving conditions, this paper presents a corpus collected ‘in-the-wild’. We propose a cross-modal attention method enhancing multi-angle AVSR for vehicles, leveraging visual context to improve accuracy and noise robustness. Our proposed model achieves state-of-the-art (SOTA) results with 98.65% accuracy in recognizing driver voice commands. For more details, visit our project page.

Language: English
DOI
Text on another site
Keywords: VisualizationSignal processingBenchmark testingAttention MechanismNoise robustnessHuman computer interaction (HCI)Audio-visual speech recognitionFeature-level fusionSpeech recognitionNoise measurementMulti-modal signal processingSpatio-temporal features

In book

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)
IEEE, 2024.
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