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相关概念视频

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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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...
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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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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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相关实验视频

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使用基于注意力的网络和规范化的特征选择来进行言语情感分类.

Samson Akinpelu1, Serestina Viriri2

  • 1School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, 4000, South Africa.

Scientific reports
|July 25, 2023
PubMed
概括

这项研究介绍了一种基于注意力的深卷积神经网络,具有RNCA特征选择,用于改进语音情感分类 (SEC). 该模型实现了97.8%的准确性,超过了现有方法.

科学领域:

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

背景情况:

  • 语音情感分类 (SEC) 对人机交互 (HCI) 和情感计算至关重要.
  • 现有的深度神经网络 (DNN) 模型面临着多语言数据和其他影响精确情绪识别的因素的挑战.
  • 注意力机制在基于序列和时间序列的任务中表现有希望.

研究的目的:

  • 建议使用基于注意力的网络改进语音情感分类模型.
  • 整合预训练的卷积神经网络 (CNN) 与规范化的邻域组件分析 (RNCA) 进行特征选择.
  • 为了提高语音情感识别的准确性和性能.

主要方法:

  • 开发了一个基于注意力的深层卷积神经网络 (DCNN).
  • 使用规范化的邻里组件分析 (RNCA) 进行特征选择.
  • 在TESS数据集上使用支持矢量机 (SVM),多层感知器 (MLP) 和随机森林 (RF) 分类器进行实验.

主要成果:

  • 提出的基于注意力的DCNN+RNCA+RF模型实现了97.8%的分类准确度.
  • 与最先进的SEC方法相比,表现有3.27%的性能改善.
  • 注意力机制和特征选择方法与人类情感感知模式保持一致.

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结论:

  • 以注意力为基础的DCNN与RNCA特征选择为语音情感分类提供了卓越的性能.
  • 该模型的有效性突出了注意力机制的潜力,以及在情感计算中强大的特征选择.
  • 这种方法提供了一种更一致,更准确的方法来识别听觉语音中的情绪.