Papers › Recreating Neural Activity During Speech Production with Language and Speech Model Embeddings
Recreating Neural Activity During Speech Production with Language and Speech Model Embeddings
Owais Mujtaba Khanday, Pablo Rodroguez San Esteban, Zubair Ahmad Lone, Marc Ouellet, Jose Andres Gonzalez Lopez
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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.
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