Low-Cost Implementation of a Named Entity Recognition System for Voice-Activated Human-Appliance Interfaces in a Smart Home

Abstract

When we develop voice-activated human-appliance interface systems in smart homes, named entity recognition (NER) is an essential tool for extracting execution targets from natural language commands. Previous studies on NER systems generally include supervised machine-learning methods that require a substantial amount of human-annotated training corpus. In the smart home environment, categories of named entities should be defined according to voice-activated devices (e.g., food names for refrigerators and song titles for music players). The previous machine-learning methods make it difficult to change categories of named entities because a large amount of the training corpus should be newly constructed by hand. To address this problem, we present a semi-supervised NER system to minimize the time-consuming and labor-intensive task of constructing the training corpus. Our system uses distant supervision methods with two kinds of auto-labeling processes: auto-labeling based on heuristic rules for single-class named entity corpus generation and auto-labeling based on a pre-trained single-class NER model for multi-class named entity corpus generation. Then, our system improves NER accuracy by using a bagging-based active learning method. In our experiments that included a generic domain that featured 11 named entity classes and a context-specific domain about baseball that featured 21 named entity classes, our system demonstrated good performances in both domains, with F1-measures of 0.777 and 0.958, respectively. Since our system was built from a relatively small human-annotated training corpus, we believe it is a viable alternative to current NER systems in smart home environments.

Application of Affordance Factors for User-Centered Smart Homes: A Case Study Approach

“…Many studies on smart homes have focused on technologies that can be applied to provide more intelligent services. Studies have been conducted on recognizing users' movements, locations, and behavioral patterns using sensors and cameras [9][10][11][12][13], or using voice control or remote control for smart homes based on the IoT and cloud computing [14][15][16][17]. Research has also been conducted on methods for predicting and providing services to users by collecting information about context, using methods such as machine learning [18][19][20][21].…”

Development of Smart Healthcare System Based on Speech Recognition Using Support Vector Machine and Dynamic Time Warping

“…The evaluation module gives accuracy, precision, recall, and F1 metrics that collectively show how good the model is based on the test to the evaluate module. The precision, recall, and F1 score are calculated as in Equations (4)- (6). The most difficult problem that we solved was enhancing recognition (i.e., microphone problems when there are different distances).…”

“…This paper presents a proposed speech recognition system to control appliances at smart homes or smart hospitals. Many researchers have developed systems that depend on vocal commands, such as wheelchair commands for people with dysarthria [4] and vocal control of appliances and devices [6]. Voice-based systems can provide a smart home with a speech recognition system with features to help visually impaired and elderly people to control devices [7].…”

A Critical Review of Smart Residential Environments for Older Adults With a Focus on Pleasurable Experience

“…According to those studies, smart technologies take into consideration direct communication with friends and family, which facilitates the improvement of health and supports the emotional balance of older adults. Park and Kim (2018) introduced a semi-supervised named entity recognition system for extracting execution targets from natural language commands. They focused on voice-activated human-interface systems in a smart home.…”