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September 4, 2026
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Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.
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RusLan-M: Technical Design and Processing Pipeline of a Longitudinal Multimedia Corpus of Russian Child Speech

P. 338–344.
Lelik V., Diachkova M., Dorofeeva S., Lopukhina A., Dragoy O., Sekerina I. A.

This paper presents RusLan-M (v.1.0), an open-access longitudinal multimedia corpus of spontaneous early child speech in Russian, designed for corpus-based research. The corpus consists of video recordings of naturalistic childcaregiver interaction, transcribed and annotated in the CHAT format and publicly available in the CHILDES database. We describe the process of corpus creation, data collection, cleaning and anonymization procedures, as well as transcription and annotation principles. The current version comprises 41 hours of recordings and over 35,000 child utterances, enabling the investigation of longitudinal trajectories of lexical growth, morphological development, syntactic complexity, and patterns of child-directed speech in Russian. Future development directions include corpus expansion with additional longitudinal datasets, systematic manual validation of automatic annotation tiers, and further integration of automatic alignment and annotation tools (BatchAlign2) adapted to Russian child speech. RusLan-M is designed for research on language acquisition, corpus linguistics, and the development of computational methods for morphologically rich languages. 

Language: English
DOI
Text on another site
Keywords: language acquisitionMultimedia corpusLongitudinal data Russian Language child speech corpus CHILDESCHATBatchAlign2
Publication based on the results of:
Neural foundations of language and its impairments: From basic research to clinical practice (2025)

In book

Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2026”
M.: Russian State University for the Humanitie, 2026.
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