HSE Researchers Create New Corpus of Early Child Speech in Russian
Researchers at the HSE Centre for Language and Brain have presented RusLan-M, an open multimedia corpus that makes it possible to trace the development of early child speech in Russian from first words to the emergence of complex grammatical constructions. The database contains around 41 hours of video recordings and more than 35,000 child utterances. The new resource will help researchers study more precisely how children acquire Russian and, in the longer term, develop more reliable tools for assessing speech development. The study has been published in Language Resources and Evaluation.
Despite the widespread use of Russian, data on its early acquisition remain limited. The international CHILDES database brings together hundreds of collections covering more than 40 languages, but Slavic languages are significantly underrepresented. At the time the study was prepared, only two corpora of children’s Russian speech were available in CHILDES. The new RusLan-M resource is intended to address this gap by providing researchers with detailed, standardised, and accessible data on how children acquire Russian.
The corpus is based on observations of two Russian-speaking children, Tosya and Yasha. Researchers at the HSE Centre for Language and Brain recorded the development of their speech over an extended period, rather than comparing children of different ages at a single point in time. The collection comprises 288 recordings: Tosya was recorded from the age of 10 months to 3 years and 10 months, while Yasha was recorded from 1 year and 4 months to 3 years of age. The total duration of the video recordings was 2,454 minutes, or around 41 hours, while the number of child utterances reached 35,386.
All recordings were accompanied by transcripts in the international CHAT format and published on TalkBank, an international resource for storing and analysing speech data. When publishing the corpus, the researchers complied with requirements concerning personal data protection and the anonymisation of personal information.
A key feature of the new resource is that it makes it possible to study children’s speech in a natural environment. Unlike laboratory experiments or parental questionnaires, the video recordings capture children’s spontaneous interactions with people close to them and their everyday linguistic environment. This makes it possible to analyse not only what children say, but also the speech context in which their language develops.
Mariia Diachkova
‘It was important for us to create a resource that would allow us to see the development of children’s speech not as a set of individual test results, but as a living and continuous process. Thanks to the longitudinal recordings, we can trace how an individual child’s speech gradually changes, which grammatical constructions emerge earlier and which later, and how the linguistic environment influences development. Such open and comprehensively annotated data is particularly important for Russian,’ said Mariia Diachkova, Research Assistant at the HSE Centre for Language and Brain and one of the study’s authors.
Pilot studies have demonstrated how RusLan-M data can be used to analyse the development of morphology and syntax over time. The corpus makes it possible to track the emergence and increasing complexity of specific linguistic constructions, compare different stages of speech development, and formulate new hypotheses about the mechanisms of language acquisition.
Valeriia Lelik
According to the authors, the significance of such data extends beyond fundamental linguistics. ‘Corpus-based measures can be used to develop normative benchmarks for speech development at different ages. In the future, this could help specialists distinguish more accurately between individual patterns of language acquisition and potential difficulties, as assessments would be based on data from children’s real spontaneous speech,’ notes Valeriia Lelik, Research Assistant at the Centre for Language and Brain and one of the study’s authors.
The authors emphasise that the new corpus partly addresses the significant shortage of open data on the development of the Russian language.
Svetlana Dorofeeva
‘Diary records of child speech have been used as research material in Russian developmental linguistics for decades. Everyone working in this field knows how much effort is required to create corpora of child speech. However, it is important to note that no multimodal corpora of child speech in Russian comparable to RusLan-M—incorporating audio, video, and text and featuring detailed linguistic annotation—had previously been publicly available. Thanks to this large-scale effort, the research community now has access to this valuable tool,’ said Svetlana Dorofeeva, project lead and Senior Research Fellow at the HSE Centre for Language and Brain.
The corpus was created through the joint efforts of a large team of parents, linguists (both researchers and students), and programmers. It brings together first-hand parental experience, academic expertise, and advanced digital technologies.
Olga Dragoy
‘In the longer term, expanding collections of this kind will make it possible to compare the speech development of a larger number of children, identify age-related patterns, and create more accurate models of language acquisition,’ said Olga Dragoy, one of the study’s authors and Director of the HSE Centre for Language and Brain.
RusLan-M also demonstrates how corpus linguistics methods can transform years of observations of children’s speech into a resource for a wide range of research, from theoretical linguistics to psychology and the practical assessment of speech development.
See also:
Window into the Body: Scientists Develop Neural Network to Detect Risk of 15 Diseases from Retinal Images
Russian universities, with the participation of HSE University, Sber, and Z-union, have developed a neural network that can simultaneously assess the risk of 15 types of pathology from retinal photographs, including not only eye diseases but also cardiovascular conditions. The AI system can help clinicians detect potentially concerning changes at an early stage, identify signs reflecting the condition of retinal blood vessels, and determine whether a patient may need further examination. The paper has been published in Frontiers in Medicine.
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.
Researchers Develop Methodology to Assess the Quality of Legal Representation in Criminal Proceedings
Having a good defence attorney in criminal proceedings can largely determine whether a defendant retains their freedom, health and good name. Researchers at HSE University propose a method for predicting an attorney’s performance based on the outcomes of their previous cases. The methodology takes into account the severity of the charges, the complexity of the cases, and the most likely outcome, drawing on judicial statistics.
When Pictures Hinder Understanding: Illustrations May Impede Learning of Abstract Ideas
Illustrations can help remember specific actions but do not always make abstract ideas easier to learn. Researchers from HSE University and Humboldt University compared how people learn from texts with different levels of abstractness. They found that participants remembered illustrations better and performed better on related tasks after reading a multimedia text about yoga asanas than after reading an abstract text about the Nash equilibrium. The findings could help improve the selection of illustrations for educational and informational materials. The study has been published in Learning and Instruction.
HSE University to Develop Predictive Analytics System for Icebreaker Motors
Industrial automation is one of the key applications of artificial intelligence. A predictive analytics system for large electric motors is among the solutions being developed for the industry as part of HSE University’s Strategic Technological Project ‘Multi-Agent Platform of AI Solutions for Industry-Specific Tasks.’ What is predictive analytics, how can it improve the operation of electric motors, and what specialists joined forces to develop this technology? Anton Zarubin, Dean of the School of Computer Science, Physics, and Technology at HSE University–St Petersburg and the project development coordinator, explains in this interview with the HSE News Service.
How to Assess Students’ Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.
Biologists Discover 'Molecular Fingerprint' of Preeclampsia
Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.