Recreating Neural Activity During Speech Production with Language and Speech Model Embeddings
Year: 2025
Type: conference-paper
Abstract: Understanding how neural activity encodes speech and language production is a fundamental challenge in neuroscience and artificial intelligence. This study investigates whether embeddings from large-scale, self-supervised language and speech models can effectively reconstruct high-gamma neural activity characteristics, key indicators of cortical processing, recorded during speech production. We leverage pre-trained embeddings from deep learning models trained on linguistic and acoustic data to represent high-level speech features and map them onto these high-gamma signals. We analyze the extent to which these embeddings preserve the spatio-temporal dynamics of brain activity. Reconstructed neural signals are evaluated against high-gamma ground-truth activity using correlation metrics and signal reconstruction quality assessments. The results indicate that high-gamma activity can be effectively reconstructed using large language and speech model embeddings in all study participants, generating Pearson's correlation coefficients ranging from 0.79 to 0.99.
Source: Interspeech (USB)
Authors: Owais Mujtaba Khanday, Pablo Rodríguez San Esteban, Zubair Ahmad Lone, Marc Ouellet, Jose A. Gonzalez-Lopez
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4415433341