通过深度学习技术增强情绪智能
B V Gokulnath1, Pampana Charmitha2, Pampana Chathurya2
1School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, 522237, India. gokulnath.b@vitap.ac.in.
Scientific reports
|December 3, 2025
概括
一组深度学习模型,包括CNN,BERT,RNN和GAN,与单个模型相比,显著改善了人类情绪识别和适应性反应. 这种多模式的方法可以增强人工智能对各种应用的同理心.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 人与计算机的交互
背景情况:
- 当前的人工智能系统经常与微妙的人类情感识别作斗争.
- 单个深度学习模型在捕捉情绪状态的复杂性方面存在局限性.
- 多模式方法正在成为更复杂的人工智能的一个有希望的方向.
研究的目的:
- 提出和评估一组深度学习模型,以提高人类情绪识别和适应性反应.
- 引入一个多式联络情感智能系统,整合各种神经网络架构.
- 证明综合模型方法比单个模型方法的优越性.
主要方法:
- 开发了一种多模式系统,将卷积神经网络 (CNN) 用于面部情绪检测,双向编码器从变压器 (BERT) 来进行文本情绪分析,循环神经网络 (RNN) 用于时间情绪跟踪,以及生成对抗网络 (GAN) 用于内容生成.
- 使用TensorFlow,Keras和PyTorch框架进行模型开发.
- 在Kaggle数据集上训练模型,包括面部表情的FER-2013和情绪分析的标记文本数据.
主要成果:
- 在面部情绪识别方面,CNN的准确性约为80%.
- 在文本情绪分析中,BERT显示了约92%的准确性.
- 对于连续情绪跟踪,RNN的准确性达到89%左右.
- 在90%以上的案例中,GANs生成了个性化,与年龄相关的内容,在上下文中被认为是适当的.
结论:
- 与单一模型方法相比,一组深度学习模型在情绪智能方面提供了更高的准确性和适应性.
- 拟议的多式联络框架为识别和响应人类情绪提供了一个强大的解决方案.
- 这项技术在医疗保健,客户服务,教育和数字福利等领域有潜在的应用,以培养更富同情心的AI.
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