Augmented Reality and Speech Control from Automobile Showcasing

Priya, S. Shanmuga; Rachana, P; Chellani, Deepansh

doi:10.1109/icssit53264.2022.9716534

Cited by 5 publications

(1 citation statement)

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“…Voice can be much more economical than typing on a keyboard. Recent research [9] indicated that augmented reality is becoming an expanding field in research and practice due to its ability to link virtual information with the real world. Significant advances in wearable devices or smartphone technology have drawn people's attention to augmented reality and have led to the rapid development of applications, games, and environments where daily human activities can be executed [10].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Peña-Cáceres,

Silva-Marchan,

Albert

et al. 2023

CMC

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The development of artificial intelligence (AI) and smart home technologies has driven the need for speech recognition-based solutions. This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes. Speech recognition allows users to control devices and perform everyday actions through spoken commands, eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light, heating, or lowering the blinds. The purpose of this study is to develop a speech-based classification model for recognizing human actions in the smart home. It seeks to demonstrate the effectiveness and feasibility of using machine learning techniques in predicting categories, subcategories, and actions from sentences. A dataset labeled with relevant information about categories, subcategories, and actions related to human actions in the smart home is used. The methodology uses machine learning techniques implemented in Python, extracting features using CountVectorizer to convert sentences into numerical representations. The results show that the classification model is able to accurately predict categories, subcategories, and actions based on sentences, with 82.99% accuracy for category, 76.19% accuracy for subcategory, and 90.28% accuracy for action. The study concludes that using machine learning techniques is effective for recognizing and classifying human actions in the smart home, supporting its feasibility in various scenarios and opening new possibilities for advanced natural language processing systems in the field of AI and smart homes.

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Section: Introductionmentioning

confidence: 99%