A Similarity-Based Approach to Recognizing Voice-Based Task Goals in Self-Adaptive Systems

Abstract

With the development of the natural language processing (NLP) technologies, users tend to directly input their goals via natural language to a task system. Thus, how to input informal voice-based task goals to self-adaptive systems (SASs) has become a challenge issue. Our previously proposed framework V-SFSA (voice-driven software fuzzy self-adaptation) can realize to input voice-based task goals to SAS. However, it still suffers from low efficiency of recognition. In this paper, in order to improve on our previous V-SFSA framework, we propose a similarity-based NLP approach to recognizing the voice-based task goals in SASs. It uses the verb of the raw voice inputs to preselect the semantic relevant commands, and then to compute the similarity between the preselected commands and predefined featured commands in a SAS. The command with the highest similarity score is accepted as the intended goals to drive a SAS. We establish the improved V-SFSA, and implement the algorithm of similarity-based fuzzy adaptation. In addition, we construct a prototype to conduct a case study. The result shows that our approach is effective.