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Multi-Interaction: A New Open-Access Approach for Studying Multimodal Communication and Interactions across Species

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Comparative studies of multimodal interactions currently suffer from a lack of consistent and standardized methods. To address this limitation, we introduce a unified framework supported by a new method for annotating, processing, and analyzing interaction data, while preserving key features such as interaction units, emitters, overlaps, and silences.

In this transcription of a hypothetical dyadic communicative interaction, the units emitted by individuals 1 and 2 appear along a temporal axis (a). The interaction can be divided into ‘states’ (orange boxes) to reflect any change when it occurs (i.e. a unit disappears or a new one appears; b). The states are then grouped into a single sequence after applying our transcription method, whereby each state corresponds to a cell with each units attached to their emitter (c).

Ultimately, this approach enables robust comparisons of interactions across different species.

Habib-Dassetto, L., P. Best, J. Cauzinille, et al.
2026. Animal Behaviour 234 (April): 123526  —  @HAL

Prof. Usha Goswami

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The ILCB is delighted to welcome Usha Goswami (University of Cambridge), a leading figure in cognitive developmental neuroscience and educational research. A member of the ILCB International Advisory Board, she was recently awarded the prestigious Doctor Honoris Causa by Aix-Marseille University.

Usha Goswami is Professor of Cognitive Developmental Neuroscience and Director of the Centre for Neuroscience in Education at the University of Cambridge. Her research has been instrumental in advancing our understanding of reading development and developmental dyslexia, particularly through a focus on the role of speech rhythm and phonological processing.Her work has received international recognition, including the Yidan Prize for Education Research (2019), and has had a major impact on both scientific knowledge and educational practice.

Usha Goswami is currently spending a three-month sabbatical at the ILCB/LPL, providing a valuable opportunity for exchanges with researchers and students. She has also just been awarded a new ERC Advanced Grant (2026–2030) on the early identification of dyslexia and developmental language disorder through speech production.

Topological data analysis of human vowels: Persistent homologies across representation spaces

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Topological data analysis is a mathematical framework that characterizes the global shape of complex data by identifying structural features—such as clusters, loops, or holes—that remain stable despite noise or small variations. We studied how vocal signals, example shown in (a), should be represented so that topology-based algorithms can correctly classify it.

We compared the topological features extracted from several signal representations, including spectrograms (b), spectrogram zeros (c), and Takens’ embeddings (d). Using a publicly available dataset of 11,200 recorded vowel utterances, we conducted an empirical analysis demonstrating that these topological features provide additional discriminative information for both speaker and vowel classification. Moreover, features derived from different signal representations appear to be complementary. Our results suggest that low-persistence topological features, often dismissed as “topological noise”, encode important information about speech.

Guillem Bonafos, Pierre Pudlo, Jean-Marc Freyermuth, Samuel Tronçon, and Arnaud Rey.
2026.
Speech Communication 178 (March): 103363 —  @HAL