A multimodal neuroimaging dataset for investigating speech perceptual normalization

A central challenge in speech perception is the lack of a one-to-one mapping between acoustic patterns and linguistic interpretations. This is often resolved through intrinsic normalization, where acoustic cues mutually influence each other's categorization. Notably, segmental (e.g., consonants, vowels) and suprasegmental (e.g., tone) features overlap temporally during speech perception, giving rise to complex interactions across linguistic and acoustic levels. However, the neural basis of these interactions remains underexplored due to a lack of integrated neuroimaging datasets designed for this purpose. This dataset presents a multimodal neuroimaging resource comprising structural MRI (sMRI), resting-state fMRI (rs-fMRI), categorization task-based fMRI, diffusion MRI (dMRI), and behavioral data from 28 participants (14 females, mean age 20.79 ± 1.52 years). Each participant completed two separate two-alternative forced-choice categorization tasks using 7 × 7 consonant-tone and vowel-tone continua. This resource is uniquely valuable for its explicit design to capture interactions between segmental and suprasegmental features, enabling researchers to explore neural representations, functional connectivity, and white matter correlates of speech normalization processes.