DESCo: Detecting Emotions from Smart Commands

Guha, Sunanda; Iqbal, Razib

doi:10.1109/compsac54236.2022.00257

Cited by 2 publications

(1 citation statement)

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“…Guha [23] have presented to Detecting User Emotions from Audio Conversations with the Smart Assistants. Here, aims to add to current body of knowledge on emotion recognition using voice commands in smart home applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling and Sentiment Analysis of Social Relationships in Elderly Smart Homes Based on Graph Neural Networks

Qianqian Hu

2024

jes

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With the expansion of Speech Emotion Recognition in the consumer domain, several devices, particularly those designed for managing smart home personal assistants for the elderly, have been widely available on the market. The increasing processing power and connection, together with the growing need to facilitate longer residency through technological interventions, highlight the potential benefits of smart home assistants. Enabling these assistants to recognize human emotions would greatly improve user-assistant communication, allowing the assistant to deliver more constructive and customized feedback to the user. In this research work, Modeling and Sentiment Analysis of Social Relationships in Elderly Smart Homes Based on Graph Neural Networks (SASR-MBHNN-BBOA) is proposed. The input data are collected from Social Recommendation Dataset. Then, input data are pre-processed utilizing Inverse Optimal Safety Filters (IOSF) for cleaning the data and removing the background noise. Then the pre-processed data are given to Memristive Bi-neuron Hopfield Neural Network (MBHNN) for predicting the sentiments like positive, negative and neutral. In general, MBHNN doesn’t express some adoption of optimization approaches for determining optimal parameters to predicting the sentiments accurately. Hence BBOA is proposed to optimize MBHNN classifier which precisely predicts the sentiments in elderly smart home. The proposed SASR-MBHNN-BBOA method is implemented in Python, and it assessed with numerous performance metrics such as accuracy, precision, recall, F1-score, ROC. The outcomes show SASR-MBHNN-BBOA attains 20.8%, 19.5%, and 29.6% higher Accuracy, 28.8%, 22.5%, and 32.6% higher Precision, 15.5%, 27.4%, and 18.2% higher Recall are analysed with existing methods such as, Emotional speech analysis in real time for smart home assistants.(SASR-CNN-SHA), Machine Learning to Investigate Elderly Care Requirements in China via the Lens of Family Caregivers (SASR-ML-IECR),Identifying User Emotions via Audio Conversations with Smart Assistants (SASR-DNN-EASA) methods respectively.

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Section: Literature Reviewmentioning

confidence: 99%