Exemplary echoic speeches for Experiment 1.

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.

语音识别的核心依赖于语音中的慢时间调制（<16 Hz）。然而近期研究表明，在线会议场景中常见的长延迟回声会消除语音中的关键时间调制，但并不会影响语音可懂度。本研究针对其背后的神经机制展开了探究。脑磁图（Magnetoencephalography, MEG）实验结果显示，皮层活动能够有效追踪被回声消除的时间调制信号，而这一现象无法通过基础的神经适应机制完全解释。此外，相较于将回声语音作为整体进行编码的模型，能够将语音与其回声进行分离的模型，可以更好地解释大脑对回声语音的皮层响应。即便在注意力被分散的情况下，仍能观察到语音分离效应；但当分离线索（即语音精细结构）被移除时，该效应便会消失。上述结果有力表明，通过听觉流分离（stream segregation）等机制，听觉系统能够构建出不受回声影响的语音包络表征，从而支持可靠的语音识别。