Abstract
The human brain's ability to transform acoustic speech signals into rich linguistic representations has inspired advancements in automatic speech recognition (ASR) systems. While ASR systems now achieve human-level performance under controlled conditions, prior research on their parallels with the brain has been limited by the use of biologically implausible models, narrow feature sets, and comparisons that primarily emphasize predictability of brain activity without fully exploring shared underlying representations. Additionally, studies comparing the brain to text-based language models overlook the acoustic stages of speech processing, an essential part in transforming sound to meaning. Leveraging high-resolution intracranial recordings and a recurrent ASR model, this study bridges these gaps by uncovering a striking correspondence in the hierarchical encoding of linguistic features, from low-level acoustic signals to high-level semantic processing. Specifically, we demonstrate that neural activity in distinct regions of the auditory cortex aligns with representations in corresponding layers of the ASR model and, crucially, that both systems encode similar features at each stage of processing-from acoustic to phonetic, lexical, and semantic information. These findings suggest that both systems, despite their distinct architectures, converge on similar strategies for language processing, providing insight in the optimal computational principles underlying linguistic representation and the shared constraints shaping human and artificial speech processing.