Complex Mapping between Neural Response Frequency and Linguistic Units in Natural Speech
Yuran Zhang, Jiajie Zou, Nai Ding

TL;DR
This study investigates how neural responses track linguistic units in natural speech, revealing that frequency-based methods cannot distinctly separate responses to different units like syllables or words.
Contribution
It demonstrates that frequency-domain analysis alone cannot reliably differentiate neural responses to various linguistic units in natural speech.
Findings
Neural responses to phones, syllables, and words correlate with speech envelope at 3-6 Hz and below 1 Hz.
1-Hz correlation mainly arises from pauses in speech.
Frequency-based decomposition is insufficient to distinguish responses to different linguistic units.
Abstract
When listening to connected speech, human brain can extract multiple levels of linguistic units, such as syllables, words, and sentences. It has been hypothesized that the time scale of cortical activity encoding each linguistic unit is commensurate with the time scale of that linguistic unit in speech. Evidence for the hypothesis originally comes from studies using the frequency-tagging paradigm that presents each linguistic unit at a constant rate, and more recently extends to studies on natural speech. For natural speech, it is sometimes assumed that neural encoding of different levels of linguistic units is captured by the neural response tracking speech envelope in different frequency bands (e.g., around 1 Hz for phrases, around 2 Hz for words, and around 4 Hz for syllables). Here, we analyze the coherence between speech envelope and idealized responses, each of which tracks a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEEG and Brain-Computer Interfaces · Neural dynamics and brain function · Neural Networks and Applications
