Implementation of the wake word for smart home automation system

Stefanovic, Igor; Nan, Eleonora; Radin, Boris

doi:10.1109/icce-berlin.2017.8210649

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…After learning, the reasoning module determines which service to provide by observing the user behaviors and the environment up to the decision making point. The learning and reasoning algorithms reported in the literature include the rule-based system [13], hidden Markov model (HMM) [14]- [16], computational intelligence (CI) [17]- [20], and reinforcement learning (RL) [21]- [23]. Rule-based algorithms encode expert knowledge and use the engineered knowledge to reason services for given situations.…”

Section: A Learning and Reasoning Algorithmsmentioning

confidence: 99%

A Stabilized Feedback Episodic Memory (SF-EM) and Home Service Provision Framework for Robot and IoT Collaboration

Kim

2020

IEEE Trans. Cybern.

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The automated home referred to as Smart Home is expected to offer fully customized services to its residents, reducing the amount of home labor, thus improving human beings' welfare. Service robots and Internet of Things (IoT) play the key roles in the development of Smart Home. The service provision with these two main components in a Smart Home environment requires: 1) learning and reasoning algorithms and 2) the integration of robot and IoT systems. Conventional computational intelligence-based learning and reasoning algorithms do not successfully manage dynamic changes in the Smart Home data, and the simple integrations fail to fully draw the synergies from the collaboration of the two systems. To tackle these limitations, we propose: 1) a stabilized memory network with a feedback mechanism which can learn user behaviors in an incremental manner and 2) a robot-IoT service provision framework for a Smart Home which utilizes the proposed memory architecture as a learning and reasoning module and exploits synergies between the robot and IoT systems. We conduct a set of comprehensive experiments under various conditions to verify the performance of the proposed memory architecture and the service provision framework and analyze the experiment results.Index Terms-Ambient intelligence, Internet of Things (IoT), learning systems, memory architecture, service robots, Smart Home.

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Section: A Learning and Reasoning Algorithmsmentioning

confidence: 99%

A Stabilized Feedback Episodic Memory (SF-EM) and Home Service Provision Framework for Robot and IoT Collaboration

Kim

2020

IEEE Trans. Cybern.

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“…In some cases, the operations performed may lead to vocal responses or text messages [5]. Undoubtedly, there are offline speech recognition engines for this purpose [6] that could help in scenarios where the connectivity service is lacking.…”

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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Sound Source Localization Based on the Simple Cross-correlation Method and Probabilistic Neural Networks

Tsybulskaya

Пермяков

Drugov

et al. 2019

Proceedings of 14th International Conference on Electromechanics and Robotics “Zavalishin's Readings”

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Implementation of the wake word for smart home automation system

Cited by 4 publications

References 3 publications

A Stabilized Feedback Episodic Memory (SF-EM) and Home Service Provision Framework for Robot and IoT Collaboration

A Stabilized Feedback Episodic Memory (SF-EM) and Home Service Provision Framework for Robot and IoT Collaboration

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

Sound Source Localization Based on the Simple Cross-correlation Method and Probabilistic Neural Networks

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