Effects of voice harmonic complexity on ERP responses to pitch-shifted auditory feedback.

Effects of voice harmonic complexity on ERP responses to pitch-shifted auditory feedback.
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DOI:
10.1016/j.clinph.2011.04.019
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发表时间:
2011-12
期刊:
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
影响因子:
--
通讯作者:
Larson CR
Larson CR
中科院分区:
其他
文献类型:
--
作者:
Behroozmand R;Korzyukov O;Larson CR

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本研究探讨了听觉反馈中不同谐波复杂度对语音音高控制的神经机制。事件相关电位(ERP)被记录在响应+200美分音高扰动的听觉反馈的自发自然人类发声,复杂的和纯音刺激在主动发声和被动听的条件。在积极的声乐生产,ERP幅度最大的音高变化在自然的声音,适度大的非语音复杂的刺激和最小的纯音。然而,在被动听,神经反应同样大的音高变化的语音和非语音复杂的刺激,但仍大于纯音。这些发现表明,与纯音相比,自然人声和非语音复杂刺激等频谱丰富的声音更容易进行音调变化检测。发声引起的语音反馈神经反应的增加表明,在发声过程中,对自然产生的复杂声音(如人声)的感觉处理通过电机驱动的机制(例如传出复制)得到增强。这种增强可以使得音频-发声系统能够更有效地检测和校正自然人类发声的反馈中的发声错误,以维持用于说话的预期发声输出。
The present study investigated the neural mechanisms of voice pitch control for different levels of harmonic complexity in the auditory feedback. Event-related potentials (ERPs) were recorded in response to +200 cents pitch perturbations in the auditory feedback of self-produced natural human vocalizations, complex and pure tone stimuli during active vocalization and passive listening conditions. During active vocal production, ERP amplitudes were largest in response to pitch shifts in the natural voice, moderately large for non-voice complex stimuli and smallest for the pure tones. However, during passive listening, neural responses were equally large for pitch shifts in voice and non-voice complex stimuli but still larger than that for pure tones. These findings suggest that pitch change detection is facilitated for spectrally rich sounds such as natural human voice and non-voice complex stimuli compared with pure tones. Vocalization-induced increase in neural responses for voice feedback suggests that sensory processing of naturally-produced complex sounds such as human voice is enhanced by means of motor-driven mechanisms (e.g. efference copies) during vocal production. This enhancement may enable the audio-vocal system to more effectively detect and correct for vocal errors in the feedback of natural human vocalizations to maintain an intended vocal output for speaking.
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