Intelligent interface based speech recognition for home automation using android application

2015 ◽  
Author(s):  
M. Tharaniya soundhari ◽  
S. Brilly Sangeetha
Keyword(s):  
Speech Recognition ◽  
Home Automation ◽  
Android Application ◽  
Intelligent Interface

Android Application Based Real Time Home Automation

2011 ◽  
Vol 4 (7) ◽  
pp. 188-190 ◽  
Author(s):  
Kallakunta. Ravi Kumar ◽  
◽  
Shaik Akbar
Keyword(s):  
Real Time ◽  
Home Automation ◽  
Android Application

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.

scholarly journals On Distant Speech Recognition for Home Automation

2015 ◽  
pp. 161-188 ◽  
Author(s):  
Michel Vacher ◽  
Benjamin Lecouteux ◽  
François Portet
Keyword(s):  
Speech Recognition ◽  
Home Automation

CARMIE

2017 ◽  
Vol 8 (4) ◽  
pp. 21-37 ◽  
Author(s):  
Joana Lobo ◽  
Liliana Ferreira ◽  
Aníbal JS Ferreira
Keyword(s):  
Heart Failure ◽  
Chronic Disease ◽  
Speech Recognition ◽  
Older People ◽  
Home Environment ◽  
System Architecture ◽  
Usability Study ◽  
Conversational Agent ◽  
Android Application ◽  
Prescribed Medicines

The incidence of chronic diseases is increasing and monitoring patients in a home environment is recommended. Noncompliance with prescribed medication regimens is a concern, especially among older people. Heart failure is a chronic disease that requires patients to follow strict medication plans permanently. With the objective of helping these patients managing information about their medicines and increasing adherence, the personal medication advisor CARMIE was developed as a conversational agent capable of interacting, in Portuguese, with users through spoken natural language. The system architecture is based on a language parser, a dialog manager, and a language generator, integrated with already existing tools for speech recognition and synthesis. All modules work together and interact with the user through an Android application, supporting users to manage information about their prescribed medicines. The authors also present a preliminary usability study and further considerations on CARMIE.

Home automation system with android application

2016 ◽  
Author(s):  
Mohd Nor Azni ◽  
L. Vellasami ◽  
A.H. Zianal ◽  
F.A. Mohammed ◽  
N.N. Mohd Daud ◽  
...  
Keyword(s):  
Automation System ◽  
Home Automation ◽  
Android Application ◽  
Home Automation System

scholarly journals A New Digital Solution Helps Automatic Voice Recognition

2018 ◽  
Vol 7 (3.4) ◽  
pp. 177
Author(s):  
M ABOULKHIR ◽  
S BOUREKKADI ◽  
S KHOULJI ◽  
K SLIMANI ◽  
M L. KERKEB
Keyword(s):  
Information Systems ◽  
Speech Recognition ◽  
Programming Language ◽  
Automatic Speech Recognition ◽  
Systems Engineering ◽  
Research Group ◽  
Voice Recognition ◽  
Scientific Work ◽  
Home Automation ◽  
Engineering Research

This scientific work concerning an examination on automatic speech recognition (ASR) frameworks connected with the home automation and to express the importance of this academic work, an itemized investigation of the engineering of speech recognition frameworks was completed. Our goal in Information Systems Engineering Research Group ofAbdelmalekEssaadi University is to choose a speech recognition programming that must work in remote speech conditions and in a rowdy area.The proposed framework is using atoolbox called Kaldi, which must correspond as aclient created by an advanced programming language, with any home automation framework. 

scholarly journals SISTEM PAKAR DIAGNOSA KERUSAKAN MOTOR KAWASAKI KLX150 MENGGUNAKAN METODE CASE-BASED REASONING DENGAN ALGORITMA 3W-JACCARD

2021 ◽  
Vol 8 (01) ◽  
pp. 1-8
Author(s):  
Akhmad Rezki Purnajaya ◽  
Fatma Indriani ◽  
Mohammad Reza Faisal
Keyword(s):  
Speech Recognition ◽  
Statistical Inference ◽  
Training Data ◽  
Case Based Reasoning ◽  
Android Application ◽  
Regional Language ◽  
Black Box Testing ◽  
Local Residents ◽  
Translation Accuracy ◽  
The Voice

Banjar language used in conversation and daily life around the area. So foreigners who come to the regions of South Kalimantan will have difficulty in communicating. Besides, most local residents in the backwoods of South Kalimantan can not use Indonesian language properly, they would be more convenient to use regional language to interact. For that reason we need an Android application can help users to find the translation of a word or phrase whenever and wherever. With the help of Google Voice Search, this application can also listen to the voice of the user to be converted into text and insert into the input translation. Speech recognition of Banjar language required a literacy training data by using the method of statistical inference to make results appropriated. Testing using method of Black Box Testing to measure the percentage of suitability of the results of translation, speech recognition for Indonesian language and speech recognition Banjar language using method of Statistical inference. So the results of translation accuracy 100% and accuracy of speech recognition Indonesian language and Banjar language by 97.85% and 82.74%.

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