IFAC PapersOnLine 51-6 (2018) 202–207 ScienceDirect Available online at www.sciencedirect.com 2405-8963 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2018.07.154 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. 1. INTRODUCTION At present, technologies are being developed in the area of building automation. Reasons for application of these standardized systems for own housing include the comfort and safety of inhabitants and economical SH operation. With regard to comfort, the control elements of building automation can be adapted to the SH user’s requirements Hajovsky (2015), (Pies 2018), (Slanina 2018). One of the possible facilitations in this area is voice control. Controlling the building in this way presents a great advantage for a certain group of people, which in particular includes seniors and disabled persons. Using voice commands, for instance, one can simply switch the light on and off, control window blinds or switch on the heating or air-conditioning. Voice commands can be implemented for any selected operational- technical function in the building. Voice control, however, has its drawbacks. Every room in the object has its own acoustics which essentially distorts the uttered command. Another and the more serious problem is that various types of additive interference may occur in some rooms, which substantially reduce the success rate of recognition of the uttered commands. This is a problem for persons who are, for health reasons, unable to manually handle the controller for e.g. the lighting, blinds, etc. At present, various methods and applications are used for voice control of the system with additive noise. The work of Agarwalla (2016) is focused on the techniques of automatic speaker recognition (ASR) and addresses learning of machines used for extraction of relevant samples from a large data volume and their application for ASR. Asano (2003) proposes a method of speech detection from multiple sound sources by means of sound and visual information in the real environment using the Bayesian network. Czyżewski and Królikowski (2001) solve the problem of processing digital audio signals by means of rough neuro hybridization. Besides that, they describe the application of soft computing methods to reduce non- stationary noise. Du (2006) deals in his work with methods based on conventional data processing, which are computationally challenging and require the knowledge of specialists for system modeling by means of Neural Networks with the subsequent utilization for signal or speech processing, image processing, analysis of data and artificial intelligence. Genaro (2009) describes the use of artificial neural networks (ANN) for the modeling of urban noise. He executed several applications at acoustically different places in Spain and compared the results with mathematical models. It was found that the ANN system was able to predict the occurrence of noise with high precision, which resulted in the improvement of these models. Gil-Pita (2012) deals with the utilization of soft computing methods for the creation of energy-efficient algorithms for binaural hearing aids able to recognize and separate speech from other undesirable audible sounds. The work of Kasabov (1998) dealt with fuzzy neural networks using the methods of structure optimization by means of a genetic algorithm together with the method of learning with forgetting for speech recognition on the phoneme basis. Malcangi and Grew (2015) deal in their work with the problem of improving automated systems for automatic speech recognition. Machacek (2011) was dealing with intelligent adaptive techniques. In this work, the ANFIS structure (Adaptive Neuro-Fuzzy Inference System) is used for the suppression of additive noise in the speech signal. The paper introduces the design, development, and verification of methodology for the assessment of processing the quality of the speech signal by means of the PESQ algorithm (standard ITU-T P.863) within voice control of operational-technical Keywords: voice control; additive noise, Smart Home (SH); ANFIS, KNX. Abstract: This paper describes utilization of the ITU-T P.863 standard for assessment of quality of the speech signal processing within voice control of operational-technical functions in Smart Home (SH) by means of the PESQ algorithm. To suppress additive noise in the speech signal in the real SH environment, the ANFIS structure is used within the SH voice control by means of the KNX bus system. The voice control of operational-technical functions of this communication bus system is a prerequisite for simpler household management, eliminating the otherwise necessary manual handling of the control device, particularly for seniors or disabled persons. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava 17 Listopadu 15, Ostrava 70833 Czech Republic (jan.vanus@vsb.cz, tomas.weiper.st@vsb.cz, radek.martinek@vsb.cz, jan.nedoma@vsb.cz, marcel.fajkus@vsb.cz, ludvik.koval@vsb.cz, roman.hrbac@vsb.cz ) J. Vanus, T. Weiper, R. Martinek, J. Nedoma, M. Fajkus, L. Koval, R. Hrbac Assessment of the Quality of Speech Signal Processing Within Voice Control of Operational-Technical Functions in the Smart Home by Means of the PESQ Algorithm