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相关概念视频

Cognitive Theories: Schachter-Singer Theory of Emotion01:20

Cognitive Theories: Schachter-Singer Theory of Emotion

389
Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
Physiological Arousal and Cognitive Labeling
According to this theory, when an individual experiences...
389
Emotional Expression01:26

Emotional Expression

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Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
Universal Facial Expressions
Psychologist Paul Ekman identified seven basic...
219
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

152
Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
152
Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
456
Physiology of Emotion01:20

Physiology of Emotion

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The physiology of emotions is a multifaceted process involving the autonomic nervous system, brain structures, hormones, and neurotransmitters. This intricate interplay dictates how emotions manifest in the body and influence behavior.
Autonomic Nervous System
The autonomic nervous system (ANS) plays a critical role in emotional responses by regulating involuntary physiological functions. It consists of two main components: the sympathetic and parasympathetic systems. The sympathetic system...
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Labeling Emotion01:20

Labeling Emotion

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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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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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一个新的网络结构语音情感识别研究研究.

Chunsheng Xu1, Yunqing Liu1, Wenjun Song1

  • 1School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
概括

本研究介绍了一种用于语音情感识别 (SER) 的新型深度学习模型,该模型有效地整合了本地和全球声学特征. 拟议的Bi-GRU和多头注意力网络显著提高了情绪检测准确度,并在情绪分析中显示出强烈的概括性.

科学领域:

  • 人工智能的人工智能
  • 语音处理 语音处理
  • 机器学习 机器学习

背景情况:

  • 语音情感识别 (SER) 对人机交互至关重要.
  • 提取相关的声学特征是SER性能的关键.
  • 目前的方法很难将分散的本地和全球情绪信息整合到语音信号中.

研究的目的:

  • 开发一种新的网络模型,用于在SER中进行全面的声学特征提取.
  • 为应对将本地和全球情感信息整合到语言中的挑战.
  • 为了提高语音情感识别和情感分析的准确性和概括性.

主要方法:

  • 提出了一个深度学习模型,集成一个封闭的循环单元 (GRU) 和多头注意力.
  • 使用双向GRU (Bi-GRU) 来捕捉时间依赖.
  • 采用多头注意力,专注于突出的声学特征.
  • 在IEMOCAP和Emo-DB语音情感体上评估模型.
  • 在CH-SIMS和MOSI语音情绪分析数据集上测试了概括.

主要成果:

  • 双GRU和多头注意力模型在SER的多个评估指标上显著超过了传统方法.
  • 该模型在语音情感分析任务中展示了出色的概括能力.
关键词:
这是一个双-GRU.多头注意力多头注意力频谱图是指光谱图中的光.语音 情感 识别 语音 情感 识别

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  • 在检测语音信号中的情绪细微差别方面取得了卓越的表现.
  • 结论:

    • 拟议的双GRU和多头注意力模型有效地整合了本地和全球声学信息,以改善SER.
    • 这种方法在语音情感识别和情感分析方面取得了重大进展.
    • 该模型的强大性能突出显示了其在理解语言中的情感内容的真实应用中的潜力.