Language-specific Tonal Features Drive Speaker-Listener Neural Synchronization

Verbal communication transmits information across diverse linguistic levels, with neural synchronization (NS) between speakers and listeners emerging as a putative mechanism underlying successful speech exchange. However, the specific speech features that drive this synchronization, and how language-specific versus universal characteristics facilitate information transfer, remain poorly understood. We developed a novel feature-based interbrain encoding modeling approach to disentangle the contributions of acoustic and linguistic features to speaker-listener NS during Mandarin storytelling and listening, as measured via magnetoencephalography (MEG). A female speaker and 22 listeners (12 females and 10 males) were recruited and analyzed. We observed strong NS across frontotemporal-parietal networks, with systematic time lags between the speaker and listeners. Crucially, suprasegmental lexical tone features (i.e., tone categories, pitch height, and pitch change), which are essential for lexical meaning in Mandarin, contributed more significantly to NS than either acoustic elements or universal segmental units (i.e., consonants and vowels). These tonal features produced unique spatiotemporal NS patterns, forming language-specific interbrain neural connections that enabled effective transmission of representations between the speaker and listeners. The strength and patterns of NS, driven by these speech features, further predicted listeners' understanding of the speaker’s storytelling. These findings demonstrate the interbrain neural mechanisms underlying shared representations during verbal exchange and highlight how language-specific speech features shape neural alignment between speakers and listeners, supporting information transfer. Significance Statement Human communication depends on shared neural representations between speakers and listeners, but the specific features that promote this alignment remain unclear. Using MEG and a feature-based interbrain analysis approach, we show that speaker-listener neural synchronization is driven more by linguistic content than by acoustics alone. Language-specific lexical tones and pitch cues have a stronger influence than segmental features and can predict how effectively listeners comprehend the speaker’s stories. These findings highlight the importance of language-specific tonal information in driving interbrain alignment and introduce a new method to distinguish the roles of different speech features. The study provides insights into how production and perception systems are coordinated across brains in space and time, depending on linguistic features during the transfer of verbal information.