This study reveals the neural mechanisms underlying speech perception, particularly highlighting the hemispheric asymmetries and predictive coding mechanisms during the processing of spoken word stimuli compared to white noise. By employing electroencephalography (EEG), we conducted a two-dimensional (2D) ERP-image analysis to elucidate cortical responses on a single-trial basis, and utilized a multivariate autoregressive (MVAR) model with the renormalized partial directed coherence (rPDC) method, to construct effective brain network connections. Discriminative features of the EEG responses to word and white noise were identified in the primary auditory regions with a hemispheric bias and the higher cognitive regions with an early regulation. These findings corroborate the established left-hemisphere dominance in language processing and endorse the idea that the brain’s engagement with auditory processing is an active process, involving the prediction of the upcoming input via an inhibitory copy along the top-down pathway, where the brain generates expectations based on internal linguistic knowledge to guide and constrain the interpretation of incoming sensory data.