Phonetic visualisation: RUDN University philologist developed a manual for foreigners on learning Russian with the help of ultrasound
“For example, the opposition of hard and soft consonants, the sound [ы], ringing noisy consonants, affricates [ц] and [ч], as well as the sounds [ж] and [ш] cause the greatest difficulties. These errors can lead to misunderstandings: for example, the words ‘угол’ and ‘уголь’ sound almost the same to speakers of other languages. Traditional teaching methods, such as observing the lips and tongue in a mirror, are not always effective, as they do not allow you to see the internal movements of the language,” Svetlana Deryabina, Associate Professor of the Department of Russian Language and Teaching Methods at the Faculty of Philology of the RUDN University.
The RUDN University philologist has developed an innovative manual for learning Russian language using video ultrasound of the tongue. This method allows students to observe in real time how the tongue works when pronouncing sounds and syllables. Ultrasound recordings made with the Articulate Assistant Advanced programme show how the tongue moves when articulating vowel and consonant sounds, with special attention paid to syllables with hard and soft consonants. The manual helps not only foreign students, but also future Russian language teachers, allowing them to better understand the causes of errors and correct them more effectively.
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Imagine a world where everyone has enough food, clean water, access to education, and decent work. A world where nature is protected and the future of our planet is cared for. These are the Sustainable Development Goals—to achieve a sustainable future for all! To this end, in 2015, the United Nations (UN) defined 17 Sustainable Development Goals (SDGs). The SDGs are a global plan that helps countries and people work together towards a better future. All 193 UN member states have joined the plan.
Researchers from the Faculty of Artificial Intelligence at RUDN University conducted a large-scale study that revealed systemic errors in large language models (LLMs) when diagnosing depression based on text. This work, carried out in collaboration with colleagues from AIRI, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow Institute of Physics and Technology, and MBZUAI, not only identifies the problem but also lays the foundation for the creation of more reliable and secure tools for detecting depression and anxiety.
Alexandra Sentyabreva, a junior researcher at the Laboratory of Cell Technologies and Tissue Engineering at RUDN Research Institute of Molecular and Cellular Medicine at the Russian University of People's Friendship, won the competition for young scientists at the All-Russian Scientific Conference “Topical Issues of Morphogenesis in Norm and Pathology.” She was awarded the Academician A.P. Avtsyn Prize.