Identifying User Intents in Vietnamese Spoken Language Commands and Its Application in Smart Mobile Voice Interaction

摘要

This paper presents a lightweight machine learning model and a fast conjunction matching method to the problem of identifying user intents behind their spoken text commands. These model and method were integrated into a mobile virtual assistant for Vietnamese (VAV) to understand what mobile users mean to carry out on their smart-phones via their commands. User intent, in the scope of our work, is an action associated with a particular mobile application. Given an input spoken command, its application will be identified by an accurate classifier while the action will be determined by a flexible conjunction matching algorithm. Our classifier and conjunction matcher axe very compact in order that we can store and execute them right on mobile devices. To evaluate the classifier and the matcher, we annotated a medium-sized data set, conducting various experiments with different settings, and achieving impressive accuracy for both the application and action identification.