Speech and music exploit distinct intrinsic timescales of the sensorimotor system

Tue, 30 Jun 2026 00:00:00 +0000

Speech and music consistently differ in their acoustic rhythms, despite the cross-cultural diversity in their surface forms. Here, we investigate whether speech and music rhythms are rooted in distinct intrinsic timescales of the sensorimotor system, which are separately recruited to support individual communication and group synchronization, respectively. Corpus analysis revealed that the timescales dominating speech (4-8 Hz) and music rhythms (< 2 Hz) separately emerge in infant laughter, babbling and cries, and that both speech and song rhythms mature at about age three. The functional division between the two timescales is further probed through sensorimotor synchronization experiments, in which participants vocalize or tap to sound sequences presented at different rates. The rhythm produced by individuals is strongest between 4 and 8 Hz. In contrast, the produced rhythm is best synchronized among participants below 2 Hz. Collectively, these findings reveal two characteristic timescales in the human sensorimotor system, i.e., a faster (4-8 Hz) timescale reflecting resonance in individual production and a slower (<2 Hz) timescale that fosters interpersonal synchronization. The two timescales provide plausible biological basis for the rhythms of speech and music.

Efficient neural encoding of time intervals in speech and complex sound sequences

Mon, 01 Jun 2026 00:00:00 +0000

Encoding time intervals in complex sound faces dual challenges: it must be precise and cover a broad dynamic range. In speech, for example, a ten millisecond lengthening of a syllable can signal stress or phrasal boundaries, yet the syllable duration distribution is long tailed beyond 500 ms and has variable statistics across speakers. Here, we propose that the auditory cortex employs efficient coding to represent time intervals. When listeners heard syllable sequences drawn from different duration distributions, the magnetoencephalographic (MEG) response from the temporal cortex scaled with syllable duration, which is characterized using the interval response function. Crucially, this interval response function met predictions of efficient coding. Its intercept and slope adapted to the mean and variance of syllable duration, respectively, and it consistently exhibited a compressive nonlinearity that reduced response skewness, consistent with a maximum entropy code. A computational model that constantly updates the inference of duration distribution provided an algorithmic account of this efficient coding, and intracranial electroencephalogram (iEEG) data confirmed the same principles during natural speech comprehension. Together, our findings reveal an efficient neural mechanism that supports precise encoding of highly variable time intervals in complex sound sequences.

Speech | Chen Honghua

Speech and music exploit distinct intrinsic timescales of the sensorimotor system

Efficient neural encoding of time intervals in speech and complex sound sequences