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相关概念视频

Labeling Emotion01:20

Labeling Emotion

Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Non-verbal communication extends beyond gestures and facial expressions to include vocal elements known as paralanguage. Paralanguage consists of non-verbal vocal cues such as pitch, loudness, speech rate, pauses, and non-verbal vocalizations like laughter, sighs, and moans. These elements not only accompany speech but also provide critical emotional and contextual information.The Role of Paralanguage in CommunicationParalanguage adds depth to spoken language by conveying emotions and...

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相关实验视频

Updated: May 22, 2026

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
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CIRE:一个中国的EEG数据集,用于解码语音意图,其调节方式是以情感为基础.

Shengrui He1, Zhongjie Li2, Jianwu Dang1,3

  • 1Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, 300350, Tianjin, China.

Scientific data
|October 21, 2025
PubMed
概括

本研究介绍了CIRE数据集,用于使用电脑电图 (EEG) 解码语音意图. 它可以研究相同文本背后的不同意图,推进脑机接口 (BCI) 技术.

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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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相关实验视频

Last Updated: May 22, 2026

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科学领域:

  • 认知神经科学 认知神经科学
  • 神经技术的神经技术
  • 语音处理 语音处理

背景情况:

  • 解码语音意图对于大脑与计算机接口 (BCI) 的发展至关重要.
  • 现有的数据集缺乏从相同文本解码语音意图的多样性.
  • 普罗索迪克情感显著影响口语的解释.

研究的目的:

  • 介绍CIRE数据集用于口语交互的意图.
  • 促进关于解码细微语音意图的研究.
  • 为了支持BCI和认知神经科学的进步.

主要方法:

  • 从38名参与者收集了高密度 (128通道) 的EEG数据.
  • 使用Wav2vec2衍生声学嵌入用于语音刺激.
  • 应用信号处理,认知分析和机器学习用于验证.

主要成果:

  • 通过基线模型实现了68.2%的跨主题分类准确度.
  • 确定了意图差异的可解释的神经生理相关物.
  • 证明了数据集在认知神经科学和BCI中的实用性.

结论:

  • CIRE数据集解决了对多样化语音意图数据的需求.
  • 高密度EEG数据支持认知神经科学和语音BCI应用.
  • 这些发现有助于大脑启发的算法和BCI开发.