Speech Emotion Detection using IoT based Deep Learning for Health Care
Speech Emotion Detection using IoT based Deep Learning for Health Care
复制标题
使用基于物联网的深度学习进行医疗保健语音情绪检测
DOI：
10.1109/bigdata47090.2019.9005638
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Yugyung Lee
中科院分区：
文献类型：
--
作者：
Zeenat Tariq;Sayed Khushal Shah;Yugyung Lee
人类情感对于识别一个人的行为和精神状态至关重要。最近，通过语音信号的情感检测开始受到更多的关注。本文提出了一种使用语音信号检测人类情感的方法，并使用基于物联网（IoT）的深度学习实时实现，用于护理养老院中的老年人。这项研究有两个主要贡献。首先，我们已经实现了一个基于音频物联网的实时系统，在这个系统中，我们记录了人类的声音，并通过深度学习预测了情绪。第二，对于高级分类，我们设计了一个使用数据规范化和数据增强技术的模型。最后，我们使用2D卷积神经网络（CNN）创建了一个集成的深度学习模型，称为语音情感检测（SED）。我们的方法报告的最佳准确度约为95%，优于所有最先进的方法。我们进一步扩展了SED模型，将其应用于采用物联网技术的实时音频情感分析系统，用于护理养老院中的老年人。
Human emotions are essential to recognize the behavior and state of mind of a person. Emotion detection through speech signals has started to receive more attention lately. This paper proposes the method for detecting human emotions using speech signals and its implementation in real-time using the Internet of Things (IoT) based deep learning for the care of older adults in nursing homes. The research has two main contributions. First, we have implemented a real-time system based on audio IoT, where we have recorded human voice and predicted emotions via deep learning. Secondly, for advance classification, we have designed a model using data normalization and data augmentation techniques. Finally, we have created an integrated deep learning model, called Speech Emotion Detection (SED), using a 2D convolutional neural networks (CNN). The best accuracy that was reported by our method was approximately 95%, which outperformed all state-of-the-art approaches. We have further extended to apply the SED model to a live audio sentiment analysis system with IoT technologies for the care of older adults in nursing homes.
DOI：
10.1109/taslp.2014.2339736
发表时间：
2014-10-01
影响因子：
5.4
作者：
Abdel-Hamid, Ossama;Mohamed, Abdel-Rahman;Yu, Dong
通讯作者：
Yu, Dong