The channel capacity of multilevel linguistic features constrains speech comprehension

Humans are expert at processing speech but how this feat is accomplished remains a major question in cognitive neuroscience. Capitalizing on the concept of channel capacity, we developed a unified measurement framework to investigate the respective influence of seven acoustic and linguistic features on speech comprehension, encompassing acoustic, sub-lexical, lexical and supra-lexical levels of description. We show that comprehension is independently impacted by all these features, but at varying degrees and with a clear dominance of the syllabic rate. Comparing comprehension of French words and sentences further reveals that when supra-lexical contextual information is present, the impact of all other features is dramatically reduced. Finally, we esti-mated the channel capacity associated with each linguistic feature and compared them with their generic dis-tribution in natural speech. Our data reveal that while acoustic modulation, syllabic and phonemic rates unfold respectively at 5, 5, and 12 Hz in natural speech, they are associated with independent processing bottlenecks whose channel capacity are of 15, 15 and 35 Hz, respectively, as suggested by neurophysiological theories. They moreover point towards supra-lexical contextual information as the feature limiting the flow of natural speech. Overall, this study reveals how multilevel linguistic features constrain speech comprehension.

Automated summary

Humans are skilled at processing speech, but the mechanism behind it is still unknown. This study developed a framework to measure the influences of different acoustic and linguistic features on speech comprehension, finding that comprehension is impacted by these features to varying degrees, with syllabic rate having the most influence. Additionally, contextual information at the supra-lexical level significantly reduces the impact of other features. The study also estimated the channel capacity associated with each linguistic feature, revealing that they have distinct processing bottlenecks.