Abstract
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 smartphones 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 are 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.
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Notes
- 1.
Microsoft Skype Translator and AT&T Speech-to-Speech Translation.
- 2.
Wit.ai: https://wit.ai.
- 3.
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Acknowledgment
This work was supported by the project QG.15.29 from Vietnam National University, Hanoi (VNU).
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Ngo, TL. et al. (2016). Identifying User Intents in Vietnamese Spoken Language Commands and Its Application in Smart Mobile Voice Interaction. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_19
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DOI: https://doi.org/10.1007/978-3-662-49381-6_19
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