Testing of the voice communication in smart home care

This article is aimed to describe the method of testing the implementation of voice control over operating and technical functions of Smart Home Come. Custom control over operating and technical functions was implemented into a model of Smart Home that was equipped with KNX technology. A sociological survey focused on the needs of seniors has been carried out to justify the implementation of voice control into Smart Home Care. In the real environment of Smart Home Care, there are usually unwanted signals and additive noise that negatively affect the voice communication with the control system. This article describes the addition of a sophisticated system for filtering the additive background noise out of the voice communication with the control system. The additive noise significantly lowers the success of recognizing voice commands to control operating and technical functions of an intelligent building. Within the scope of the proposed application, a complex system based on fuzzy-neuron networks, specifically the ANFIS (Adaptive Neuro-Fuzzy Interference System) for adaptive suppression of unwanted background noises was created. The functionality of the designed system was evaluated both by subjective and by objective criteria (SSNR, DTW). Experimental results suggest that the studied system has the potential to refine the voice control of technical and operating functions of Smart Home Care even in a very noisy environment.

[1]  Petr Pollák,et al.  Methods for Speech SNR Estimation: Evaluation Tool and Analysis of VAD Dependency , 2005 .

[2]  Jong-Tae Lim,et al.  Robotic smart house to assist people with movement disabilities , 2007, Auton. Robots.

[3]  Hermann Hansemann Thomas Hübner Christof Merz.  Automatizované systémy budov : sdělovací systémy KNX/EIB, LON a BACnet. , 2008 .

[4]  Kune-Yao Chen,et al.  Practical design of intelligent remote-controller with speech-recognition and self-learning function , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[5]  Jan Va uš.  APPLICATION OF VARIATIONS OF THE LMS ADAPTIVE FILTER FOR VOICE COMMUNICATIONS WITH CONTROL SYSTEM , 2010 .

[6]  Radek Martinek,et al.  Software based flexible measuring systems for analysis of digitally modulated systems , 2010, 9th RoEduNet IEEE International Conference.

[7]  Radek Martinek,et al.  Use of adaptive filtering for noise reduction in communications systems , 2010, 2010 International Conference on Applied Electronics.

[8]  Shintaro Izumi,et al.  Implementing Virtual Agent as an Interface for Smart Home Voice Control , 2012, 2012 19th Asia-Pacific Software Engineering Conference.

[9]  Radek Martinek,et al.  Refining the diagnostic quality of the abdominal fetal electrocardiogram using the techniques of artificial intelligence , 2012 .

[10]  Yan Luo,et al.  Enhancing Wi-Fi fingerprinting for indoor positioning using human-centric collaborative feedback , 2013, Human-centric Computing and Information Sciences.

[11]  Prabhat Verma,et al.  A framework to integrate speech based interface for blind web users on the websites of public interest , 2013, Human-centric Computing and Information Sciences.

[12]  Jirí Koziorek,et al.  The design of the voice communication in Smart Home Care , 2013, 2013 36th International Conference on Telecommunications and Signal Processing (TSP).

[13]  Radek Martinek,et al.  Power Quality Improvement by Shunt Active Performance Filters Emulated by Artificial Intelligence Techniques , 2013, CSE 2013.

[14]  Jirí Koziorek,et al.  Design of a Smart Building Control with View to the Senior Citizens' Needs , 2013, PDeS.

[15]  Radek Martinek,et al.  The Real Implementation of ANFIS Channel Equalizer on the System of Software-Defined Radio , 2014 .