Language | Chen Honghua

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.

Low-frequency cortical activity reflects context-dependent parsing of word sequences

Thu, 08 May 2025 00:00:00 +0000

During speech listening, it has been hypothesized that the brain builds representations of linguistic structures like sentences, which are tracked by neural activity entrained to the rhythm of these structures. Alternatively, others proposed that these sentence-tracking neural activities may reflect the predictability or syntactic properties of individual words. Here, to disentangle the neural responses to sentences and words, we design word sequences that are parsed into different sentences in different contexts. By analyzing neural activity recorded by magnetoencephalography, we find that low-frequency neural activity strongly depends on context—the difference between MEG responses to the same word sequence in two contexts yields a low-frequency signal, which precisely tracks sentences. The predictability and syntactic properties of words can partly explain the neural response in each context but not the difference between contexts. In summary, low-frequency neural activity encodes sentences and can reliably reflect how same-word sequences are parsed in different contexts.

Original speech and its echo are segregated and separately processed in the human brain

Thu, 15 Feb 2024 00:00:00 +0000

Speech recognition crucially relies on slow temporal modulations (<16 Hz) in speech. Recent studies, however, have demonstrated that the long-delay echoes, which are common during online conferencing, can eliminate crucial temporal modulations in speech but do not affect speech intelligibility. Here, we investigated the underlying neural mechanisms. MEG experiments demonstrated that cortical activity can effectively track the temporal modulations eliminated by an echo, which cannot be fully explained by basic neural adaptation mechanisms. Furthermore, cortical responses to echoic speech can be better explained by a model that segregates speech from its echo than by a model that encodes echoic speech as a whole. The speech segregation effect was observed even when attention was diverted but would disappear when segregation cues, i.e., speech fine structure, were removed. These results strongly suggested that, through mechanisms such as stream segregation, the auditory system can build an echo-insensitive representation of speech envelope, which can support reliable speech recognition.

AI for Psycholinguistics

Sat, 10 Jun 2023 00:00:00 +0000

Recently, I participated in the organization of a Chinese workshop about NLP at Chinese Psycholinguistic Society in Guangzhou in 2023 (with Prof. Nai Ding, Dr. Jiajie Zou, and Wei Liu). It provides a psycholinguistic research tool based on artificial intelligence language models in natural language processing, which is convenient to extract language features and carry out language-related tasks.