scholarly journals Distant speech recognition for home automation: Preliminary experimental results in a smart home

2011 ◽  
Author(s):  
Benjamin Lecouteux ◽  
Michel Vacher ◽  
Francois Portet
Keyword(s):  
Speech Recognition ◽  
Smart Home ◽  
Experimental Results ◽  
Home Automation

scholarly journals Voice Controlled Smart Home Automation System

2019 ◽  
Vol 8 (3) ◽  
pp. 4092-4093
Keyword(s):  
User Interface ◽  
Speech Recognition ◽  
Smart Home ◽  
Cost Effective ◽  
Automation System ◽  
Home Automation ◽  
Automation Systems ◽  
Home Automation System ◽  
The Cost ◽  
Electrical Appliance

The home automation is the future and important part of a house. The home automation is highly reliable in system for controlling house electrical appliances. As technologies improves the home automation system are becoming smarter and can regulate certain tasks automatically and autonomously. The home automation systems are cost effective and reduces the consumption of energy of household and cut the cost of electricity bills. In this paper a home automation system is discussed, the system controls the electrical appliance of house by using user interface device and speech recognition technology by using micro-controller device via a Bluetooth module and a mechanical relay acting as a switch for controlling electrical appliances.

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

Smart home automation using IFTTT and google assistant

2021 ◽  
Author(s):  
S.V. Aswin Kumer ◽  
P. Kanakaraja ◽  
A. Punya Teja ◽  
T. Harini Sree ◽  
T. Tejaswni
Keyword(s):  
Smart Home ◽  
Home Automation

scholarly journals A Secure and Scalable Smart Home Gateway to Bridge Technology Fragmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3587
Author(s):  
Ezequiel Simeoni ◽  
Eugenio Gaeta ◽  
Rebeca I. García-Betances ◽  
Dave Raggett ◽  
Alejandro M. Medrano-Gil ◽  
...  
Keyword(s):  
Smart Home ◽  
Home Automation ◽  
Dynamic Configuration ◽  
Home Gateway ◽  
Security Authentication ◽  
Living Lab ◽  
Automation Systems ◽  
Authentication And Authorization ◽  
Iot Devices ◽  
Bridge Technology

Internet of Things (IoT) technologies are already playing an important role in our daily activities as we use them and rely on them to increase our abilities, connectivity, productivity and quality of life. However, there are still obstacles to achieving a unique interface able to transfer full control to users given the diversity of protocols, properties and specifications in the varied IoT ecosystem. Particularly for the case of home automation systems, there is a high degree of fragmentation that limits interoperability, increasing the complexity and costs of developments and holding back their real potential of positively impacting users. In this article, we propose implementing W3C’s Web of Things Standard supported by home automation ontologies, such as SAREF and UniversAAL, to deploy the Living Lab Gateway that allows users to consume all IoT devices from a smart home, including those physically wired and using KNX® technology. This work, developed under the framework of the EC funded Plan4Act project, includes relevant features such as security, authentication and authorization provision, dynamic configuration and injection of devices, and devices abstraction and mapping into ontologies. Its deployment is explained in two scenarios to show the achieved technology’s degree of integration, the code simplicity for developers and the system’s scalability: one consisted of external hardware interfacing with the smart home, and the other of the injection of a new sensing device. A test was executed providing metrics that indicate that the Living Lab Gateway is competitive in terms of response performance.

Smart home automation using virtue of IoT

2017 ◽  
Author(s):  
Vaibhavkumar Yadav ◽  
Shubham Borate ◽  
Soniya Devar ◽  
Rohit Gaikwad ◽  
A.B. Gavali
Keyword(s):  
Smart Home ◽  
Home Automation

FINITELY SUBSEQUENTIAL TRANSDUCERS

2003 ◽  
Vol 14 (06) ◽  
pp. 983-994 ◽  
Author(s):  
CYRIL ALLAUZEN ◽  
MEHRYAR MOHRI
Keyword(s):  
Speech Recognition ◽  
Finite Number ◽  
Efficient Algorithm ◽  
Experimental Results ◽  
Theoretical Formulation ◽  
Large Vocabulary ◽  
Finite State Transducers ◽  
Finite State ◽  
Large Vocabulary Speech Recognition

Finitely subsequential transducers are efficient finite-state transducers with a finite number of final outputs and are used in a variety of applications. Not all transducers admit equivalent finitely subsequential transducers however. We briefly describe an existing generalized determinization algorithm for finitely subsequential transducers and give the first characterization of finitely subsequentiable transducers, transducers that admit equivalent finitely subsequential transducers. Our characterization shows the existence of an efficient algorithm for testing finite subsequentiability. We have fully implemented the generalized determinization algorithm and the algorithm for testing finite subsequentiability. We report experimental results showing that these algorithms are practical in large-vocabulary speech recognition applications. The theoretical formulation of our results is the equivalence of the following three properties for finite-state transducers: determinizability in the sense of the generalized algorithm, finite subsequentiability, and the twins property.

scholarly journals Internet of Things-Based Intelligent Smart Home Control System

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Olutosin Taiwo ◽  
Absalom E. Ezugwu
Keyword(s):  
Machine Learning ◽  
Mobile Application ◽  
Smart Home ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Automation System ◽  
Home Automation ◽  
Area Of Interest ◽  
Home Automation System

The smart home is now an established area of interest and research that contributes to comfort in modern homes. With the Internet being an essential part of broad communication in modern life, IoT has allowed homes to go beyond building to interactive abodes. In many spheres of human life, the IoT has grown exponentially, including monitoring ecological factors, controlling the home and its appliances, and storing data generated by devices in the house in the cloud. Smart home includes multiple components, technologies, and devices that generate valuable data for predicting home and environment activities. This work presents the design and development of a ubiquitous, cloud-based intelligent home automation system. The system controls, monitors, and oversees the security of a home and its environment via an Android mobile application. One module controls and monitors electrical appliances and environmental factors, while another module oversees the home’s security by detecting motion and capturing images. Our work uses a camera to capture images of objects triggered by their motion being detected. To avoid false alarms, we used the concept of machine learning to differentiate between images of regular home occupants and those of an intruder. The support vector machine algorithm is proposed in this study to classify the features of the image captured and determine if it is that of a regular home occupant or an intruder before sending an alarm to the user. The design of the mobile application allows a graphical display of the activities in the house. Our work proves that machine learning algorithms can improve home automation system functionality and enhance home security. The work’s prototype was implemented using an ESP8266 board, an ESP32-CAM board, a 5 V four-channel relay module, and sensors.

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