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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...
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相关实验视频

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语音情感识别与轻量级深度神经合奏模型使用手工制作的功能.

Jaher Hassan Chowdhury1, Sheela Ramanna2, Ketan Kotecha3

  • 1The University of Winnipeg, 515 Portage Avenue, Winnipeg, Manitoba, Canada.

Scientific reports
|April 8, 2025
PubMed
概括

这项研究表明,使用手工制作的功能进行语音情感识别 (SER) 的轻量级组合模型,优于基于光谱图的方法. 仔细的微调显著提高了情绪检测任务的性能.

关键词:
音频信号处理 音频信号处理平均整体的平均值.双向的LSTM是一个双向的LSTM.卷积神经网络是一种卷积神经网络.语音 情感识别 语音 情感识别

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科学领域:

  • 人工智能的人工智能
  • 人与计算机的交互
  • 信号处理 信号处理

背景情况:

  • 语音情感识别 (SER) 对医疗保健,HCI和机器人技术至关重要.
  • 在SER的挑战包括数据稀缺和复杂的特征提取.
  • 现有的方法通常依赖于像光谱图这样的自动特征提取.

研究的目的:

  • 为了调查一个轻量级的深度神经元组合模型 (CNN & CNN_Bi-LSTM) 与手工制作的特征是否超过了SER的自动特征提取方法.
  • 评估微调技术 (如学习速度调度器和规范化) 对模型性能的影响.
  • 在多样化,公开可用的 SER 数据集上验证拟议的模型.

主要方法:

  • 开发了一个CNN和CNN_Bi-LSTM组合模型.
  • 使用了手工制作的音频功能:零交叉率 (ZCR),根平均平方误差 (RMSE),克罗玛短时间里埃变换 (STFT) 和梅尔频率塞普斯特尔系数 (MFCC).
  • 在五个数据集 (RAVDESS,TESS,SAVEE,CREMA-D,EmoDB) 中使用学习率调度器,规范化和LIME技术进行模型解释.

主要成果:

  • 拟议的整体模型始终优于单个模型和基于光谱的方法.
  • 在关键指标上取得了卓越的表现:准确性,AUC-ROC,AUC-PRC和F1得分.
  • LIME分析为模型的预测提供了可解释性.

结论:

  • 轻量级的组装模型与精心挑选的手工制作特征对SER非常有效.
  • 微调策略对于优化情绪识别中的深度学习模型至关重要.
  • 提出的方法提供了一个强大的和可解释的解决方案,用于从语音中自动检测情绪.