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首页> 外文期刊>Advances in Experimental Medicine and Biology >Robust cortical encoding of slow temporal modulations of speech.
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Robust cortical encoding of slow temporal modulations of speech.

机译:语音慢速调制的鲁棒皮层编码。

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摘要

This study investigates the neural representation of speech in complex listening environments. Subjects listened to a narrated story, masked by either another speech stream or by stationary noise. Neural recordings were made using magnetoencephalography (MEG), which can measure cortical activity synchronized to the temporal envelope of speech. When two speech streams are presented simultaneously, cortical activity is predominantly synchronized to the speech stream the listener attends to, even if the unattended, competing-speech stream is more intense (up to 8 dB). When speech is presented together with spectrally matched stationary noise, cortical activity remains precisely synchronized to the temporal envelope of speech until the noise is 9 dB more intense. Critically, the precision of the neural synchronization to speech predicts subjectively rated speech intelligibility in noise. Further analysis reveals that it is longer-latency (~100 ms) neural responses, but not shorter-latency (~50 ms) neural responses, that show selectivity to the attended speech and invariance to background noise. This indicates a processing transition, from encoding the acoustic scene to encoding the behaviorally important auditory object, in auditory cortex. In sum, it is demonstrated that neural synchronization to the speech envelope is robust to acoustic interference, whether speech or noise, and therefore provides a strong candidate for the neural basis of acoustic-background invariant speech recognition.
机译:这项研究调查了复杂听音环境中语音的神经表示。受试者听了一个叙述的故事,被另一个语音流或平稳的噪音掩盖。使用脑磁图(MEG)进行神经记录,可以测量与语音时空包络同步的皮层活动。当同时显示两个语音流时,即使无人值守,竞争性语音流更激烈(高达8 dB),皮层活动也将主要与听众参加的语音流同步。当语音与频谱匹配的固定噪声一起呈现时,皮层活动将保持与语音的时间包络精确同步,直到噪声高出9 dB。至关重要的是,语音的神经同步精度可预测噪声中主观评估的语音清晰度。进一步的分析表明,较长的(〜100 ms)神经反应,而不是较短的(〜50 ms)神经反应,表现出对参加演讲的选择性和对背景噪声的不变性。这指示了在听觉皮层中从编码声学场景到对行为上重要的听觉对象进行编码的处理过渡。总而言之,证明了与语音包络的神经同步对于声音干扰,无论是语音还是噪声,都具有鲁棒性,因此为声学背景不变语音识别的神经基础提供了强大的候选者。

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