Electrophysiological indices of hierarchical speech processing differentially reflect the comprehension of speech in noise

Abstract

The past few years have seen an increase in the use of encoding models to explain neural responses to natural speech. The goal of these models is to characterize how the human brain converts acoustic speech energy into different linguistic representations that enable everyday speech comprehension. For example, researchers have shown that electroencephalography (EEG) data can be modeled in terms of acoustic features of speech, such as its amplitude envelope or spectrogram, linguistic features such as phonemes and phoneme probability, and higher-level linguistic features like context-based word predictability. However, it is unclear how reliably EEG indices of these different speech representations reflect speech comprehension in different listening conditions. To address this, we recorded EEG from neurotypical adults who listened to segments of an audiobook in different levels of background noise. We modeled how their EEG responses reflected different acoustic and linguistic speech features and how this varied with speech comprehension across noise levels. In line with our hypothesis, EEG signatures of context-based word predictability and phonetic features were more closely correlated with behavioral measures of speech comprehension and percentage of words heard than EEG measures based on low-level acoustic features. EEG markers of the influence of top-down, context-based prediction on bottom-up acoustic processing also correlated with behavior. These findings help characterize the relationship between brain and behavior by comprehensively linking hierarchical indices of neural speech processing to language comprehension metrics.