Far-field Speech-controlled Smart Classroom with Natural Language Processing built under KNX Standard for Appliance Control

Abstract

As the world continues to embark on its era of innovation, recent technological advancements easily become integrated into people's everyday living. With the establishing premise of artificial intelligence (AI) on the horizon, technology becomes increasingly transparent to its users (termed as ambient technology) in an attempt to mimic human nature when handling complex processes and delivering efficient results. In spite of this, majority of research on voice user interfaces (VUIs) have been partial to the benefit of people with disabilities. This study overwhelms this limitation as the implementation of a VUI in a smart classroom is achieved for the purposes of ambient technology. The main objective of this study is to develop a far-field speech-controlled smart classroom with natural language processing (NLP) built under KNX standard for appliance control. Using NLP techniques such as shallow parsing and Naïve Bayes classification, the researchers were able to write their own API in Python for a VUI codenamed Luna. From the experiment conducted in this study, Luna was able to obtain a confusion matrix (CM) accuracy of 95.56% with the implementation of the Naïve Bayes classifier alongside the shallow parser.