A brain-rhythm hierarchical predictive computations integrate semantics and acoustics in speech processing

Unraveling how humans effortlessly grasp speech despite diverse environmental challenges has long intrigued researchers in systems and cognitive neuroscience. The interplay between semantic and phonological language structures has been a subject of debate in the linguistics and neurolinguistics literature that, so far, has not been resolved. We seek to understand the neural intricacies underpinning semantic-acoustic interplay in robust speech comprehension. To do so, we construct a computational mechanistic proof for the hypothesis, proposing a pivotal role for rhythmic predictive top-down contextualization facilitated by the delta rhythm in achieving time-invariant speech processing. Our Brain-Rhythm-based Inference model, BRyBI, integrates three key rhythmic processes -- theta-gamma interactions for parsing phoneme sequences, dynamic delta rhythm for inferred prosodic-phrase context, and resilient speech representations. Demonstrating mechanistic proof-of-principle, BRyBI replicates human behavioral experiments, showcasing its ability to handle pitch variations, time-warped speech, interruptions, and silences in non-comprehensible contexts. Intriguingly, the model aligns with human experiments, revealing optimal silence time scales in the theta- and delta-frequency ranges. Comparative analysis with deep neural network language models highlights distinctive performance patterns, emphasizing the unique capabilities of a rhythmic framework. In essence, our study sheds light on the neural underpinnings of speech processing, emphasizing the role of rhythmic brain mechanisms in structured temporal signal processing -- an insight that challenges prevailing artificial intelligence paradigms and hints at potential advancements in compact and robust computing architectures.