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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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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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相关实验视频

Updated: Jul 20, 2025

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使用双功能提取编码器增强语音情感识别.

Ilkhomjon Pulatov1, Rashid Oteniyazov2, Fazliddin Makhmudov1

  • 1Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea.

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|July 29, 2023
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概括

这项研究引入了一个新的语音情感识别框架,使用谱图和语义特征. 该系统实现了高精度,在基准数据集上表现优于现有模型.

关键词:
在美国,CNN是CNN.这是LSTM的LSTM.在MFCC中,MFCC是最重要的.功能提取 特性提取频谱图是指光谱图中的光谱.语音 情感 识别 语音 情感 识别

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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:

  • 准确的语音情感识别对于人机交互至关重要.
  • 现有的方法在性能和精度上有局限性.
  • 从语言中开发强大的情绪检测是一个持续的挑战.

研究的目的:

  • 开发用于语音情感识别 (SER) 的创新框架.
  • 通过解决当前方法学的不足来提高SER性能.
  • 为了提高人类语言情感解释的准确性和有效性.

主要方法:

  • 使用完全卷积神经网络进行语音谱图转录.
  • 采用Mel-frequency cepstral coefficient (MFCC) 功能提取,与语音2Vec集成用于语义编码.
  • 通过长期短期存储器 (LSTM) 网络和完全连接的层处理双特性.

主要成果:

  • 在RAVDESS数据集上获得了94.8%的准确性.
  • 在EMO-DB数据集上实现了94.0%的准确性.
  • 与已建立的SER模型相比,表现出卓越的性能.

结论:

  • 拟议的框架显著提高了语音情感识别准确度.
  • 谱图和语义特征的结合方法被证明是有效的.
  • 该系统为SER提供了更复杂,更有效的解决方案.