Speech Emotion Detection using IoT based Deep Learning for Health Care

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 realtime 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.

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