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自然语音中神经反应频率与语言单位之间的复杂映射关系。

Complex Mapping between Neural Response Frequency and Linguistic Units in Natural Speech.

机构信息

Zhejiang University, Hangzhou, China.

出版信息

J Cogn Neurosci. 2023 Aug 1;35(8):1361-1368. doi: 10.1162/jocn_a_02013.

Abstract

When listening to connected speech, the 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 single level of linguistic unit. Four units, that is, phones, syllables, words, and sentences, are separately considered. We show that the idealized phone-, syllable-, and word-tracking responses all correlate with the speech envelope both around 3-6 Hz and below ∼1 Hz. Further analyses reveal that the 1-Hz correlation mainly originates from the pauses in connected speech. The results here suggest that a simple frequency-domain decomposition of envelope-tracking activity cannot separate the neural responses to different linguistic units in natural speech.

摘要

在聆听连续语音时,人脑可以提取多个语言单位,如音节、单词和句子。人们假设,编码每个语言单位的皮质活动时间尺度与该语言单位在语音中的时间尺度相称。该假设的证据最初来自于使用频率标记范式的研究,该范式以恒定的速率呈现每个语言单位,最近的研究还扩展到了自然语音研究。对于自然语音,人们有时假设,不同语言单位的神经编码是通过跟踪不同频带中的语音包络来实现的(例如,短语的频率约为 1 Hz,单词的频率约为 2 Hz,音节的频率约为 4 Hz)。在这里,我们分析了语音包络与理想化响应之间的相干性,每个响应都跟踪单个语言单位。分别考虑了四个单位,即音素、音节、单词和句子。我们表明,理想化的音素、音节和单词跟踪响应都与 3-6 Hz 左右和低于 ∼1 Hz 的语音包络相关。进一步的分析表明,1-Hz 的相关性主要来源于连续语音中的停顿。结果表明,简单的包络跟踪活动的频域分解不能分离自然语音中不同语言单位的神经响应。

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