皮层网络用于识别与同时说话者的语音
Christian Herrera1, Nicole Whittle1, Marjorie R Leek2
1VA Loma Linda Healthcare System, Loma Linda, CA, United States.
Hearing research
|August 2, 2023
概括
这项研究揭示了在竞争声音中识别语音的独特大脑网络作用. 高层时区域处理基本的声学处理,而前层双层网络管理听力和信号退化.
科学领域:
- 神经科学是一个神经科学.
- 听觉感知是一种听觉感知.
- 认知神经科学 认知神经科学
背景情况:
- 在复杂的听觉环境中理解语音识别至关重要.
- 在处理竞争性语音时,时间与前端双双脑脑网络的具体作用尚未完全理解.
研究的目的:
- 调查上 (S-T) 和前 (F-P) 大脑网络在有或没有竞争性语音的情况下对语音识别的独特贡献.
- 使用光谱时态调制转移函数 (ST-MTF) 分析来建模大脑活动,以了解这些网络如何处理听觉信息.
主要方法:
- 功能性磁共振成像 (fMRI) 用于31名参与者执行两个竞争性语音任务 (Unison和Competing).
- 对语音混合物应用了光谱时态调制过,ST-MTF模型根据声学扭曲预测了大脑激活.
- 鉴定出了三个不同的皮质网络:S-T,FP,以及一个中枢/角环 (M-AG) 网络.
主要成果:
- S-T网络对各种条件的可理解性线索做出了反应,而FP网络对信号退化和信息掩盖表现出了普遍的反应.
- 任务性能与S-T网络激活具有正相关性,与FP网络激活具有负相关性.
- 在竞争条件下,S-T网络活动主要由声学线索驱动,而FP网络活动受到非声学因素的影响.
结论:
- 高部区域在语音识别中发挥着自下而上的感知作用,独立于与之竞争的语音.
- 前沿对称区域发挥着自上而下的作用,由竞争性语音调节,与倾听力度相关.
- 有效的语音识别取决于S-T和FP网络之间的动态相互作用,其他大脑区域的补充作用.
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