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This literature survey delves into the realm of gesture and voice recognition technologies
tailored for controlling Internet of Things (IoT) devices in smart home environments. With the
proliferation of IoT devices, the demand for intuitive and efficient control mechanisms has surged,
leading to the exploration of gesture and voice recognition as viable solutions. The survey
examines the landscape of existing research, focusing on the utilization of deep learning
techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks
(RNNs), for enhancing gesture and voice recognition accuracy. It highlights the evolution of these
technologies, their applications in smart home environments, and the challenges faced, including
environmental robustness and user privacy. By critically analyzing the strengths and limitations
of current methodologies, the survey aims to provide insights into the future of gesture and voice
recognition for IoT device control, identifying potential areas for further research and
development.
Keywords:
Deep Learning, Gesture Recognition, Voice Recognition
Cite Article:
"GESTURE AND VOICE RECOGNITION SYSTEM USING DEEP LEARNING TO CONTROL IOT APPLICATIONS: A Literature Survey", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 5, page no.263 - 267, May-2024, Available :http://www.ijrti.org/papers/IJRTI2405039.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator