Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Labeling Emotion01:20

Labeling Emotion

124
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...
124
Cognitive Theories: Schachter-Singer Theory of Emotion01:20

Cognitive Theories: Schachter-Singer Theory of Emotion

362
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...
362
Emotional Expression01:26

Emotional Expression

201
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...
201

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Multimodal Emotion Recognition Using Modality-Wise Knowledge Distillation.

Sensors (Basel, Switzerland)·2025
Same author

Postfilter for Dual Channel Speech Enhancement Using Coherence and Statistical Model-Based Noise Estimation.

Sensors (Basel, Switzerland)·2024
Same author

Improved Speech Spatial Covariance Matrix Estimation for Online Multi-Microphone Speech Enhancement.

Sensors (Basel, Switzerland)·2023
Same author

Affective Latent Representation of Acoustic and Lexical Features for Emotion Recognition.

Sensors (Basel, Switzerland)·2020
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 21, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.5K

语音情感识别 纳入相对难度和标记可靠性

Youngdo Ahn1, Sangwook Han1, Seonggyu Lee1

  • 1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Buk-gu, Gwangju 61005, Republic of Korea.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的语音情感识别 (SER) 方法,使用自定义损失函数和标签光滑. 该方法通过专注于样本难度和标签可靠性,在多样化,未见的数据集上增强SER模型的稳定性.

关键词:
概括的概括是一般化的.标签可靠性的标签可靠性在体外的外体.相对困难相对困难语音 情感 识别 语音 情感 识别

更多相关视频

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
05:51

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Published on: May 15, 2016

9.0K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K

相关实验视频

Last Updated: Jun 21, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.5K
Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
05:51

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Published on: May 15, 2016

9.0K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 语音处理 语音处理

背景情况:

  • 语音情感识别 (SER) 模型由于不同的情感表达而经常与未见的数据作斗争.
  • 现有的方法,如规范化或指标损失,旨在提高SER的稳定性.
  • 挑战来自情感上下文和影响培训数据的注释器可靠性.

研究的目的:

  • 开发一种语音情感识别 (SER) 方法,该方法对未见的 corpora. robust.
  • 将训练样本难度和标签可靠性纳入SER模型训练.
  • 在培训分布之外,在各种数据集上提高SER性能.

主要方法:

  • 提出了一种新的损失函数,灵感来自Proxy-Anchor损失,优先考虑难以分类的样本.
  • 通过预先训练的SER模型对错误分类的样品引入标签光滑,以减轻不可靠的标签.
  • 将相对样本难度和标签可靠性整合到培训过程中.

主要成果:

  • 拟议的SER方法在看不见的实体上表现得更好.
  • 新的损失函数通过专注于具有挑战性的样本来有效地指导模型.
  • 对错误分类数据的标签平滑进一步增强了模型的概括能力.

结论:

  • 拟议的SER方法增强了对未见的语音情感数据变化的模型稳定性.
  • 纳入样本难度和标签可靠性对于有效的SER至关重要.
  • 新型损失函数和标签平滑的结合为未来的SER研究提供了有希望的方向.