Laboratorio de Inteligencia Artificial Aplicada, Instituto de Ciencias de la Computación (Universidad de Buenos Aires - Consejo Nacional de Investigaciones Cientificas y Tecnicas), Buenos Aires, Argentina.
Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i) (Universidad Nacional del Litoral - Consejo Nacional de Investigaciones Cientificas y Tecnicas), Santa Fe, Argentina.
Commun Biol. 2024 Mar 8;7(1):291. doi: 10.1038/s42003-024-05945-9.
When engaged in a conversation, one receives auditory information from the other's speech but also from their own speech. However, this information is processed differently by an effect called Speech-Induced Suppression. Here, we studied brain representation of acoustic properties of speech in natural unscripted dialogues, using electroencephalography (EEG) and high-quality speech recordings from both participants. Using encoding techniques, we were able to reproduce a broad range of previous findings on listening to another's speech, and achieving even better performances when predicting EEG signal in this complex scenario. Furthermore, we found no response when listening to oneself, using different acoustic features (spectrogram, envelope, etc.) and frequency bands, evidencing a strong effect of SIS. The present work shows that this mechanism is present, and even stronger, during natural dialogues. Moreover, the methodology presented here opens the possibility of a deeper understanding of the related mechanisms in a wider range of contexts.
当进行对话时，人们会从对方的言语中接收听觉信息，也会从自己的言语中接收听觉信息。然而，这种信息会受到一种称为“语音诱导抑制”的效应的不同处理。在这里，我们使用脑电图 (EEG) 和来自两个参与者的高质量语音记录，研究了自然非脚本对话中语音的声学特性的大脑表现。使用编码技术，我们能够重现之前关于听取他人言语的广泛研究结果，并且在预测这种复杂情况下的 EEG 信号时取得了更好的表现。此外，当使用不同的声学特征（频谱图、包络等）和频带来聆听自己的声音时，我们没有发现任何反应，这证明了 SIS 的强大作用。本工作表明，在自然对话中，这种机制不仅存在，而且更加强大。此外，这里提出的方法为在更广泛的背景下深入了解相关机制提供了可能性。
