뉴스에서 시멘틱 디코딩의 음성대화시스템을 위한 히든 벡터 상태 마코브모델의 상세설계

A Detailed Design of Hidden Vector State Markov Model for Semantic Decoding of Spoken Dialogue System on News

Nowadays, Spoken Dialogue System is rapidly growing by investing a lot from researches as well as organizations. One of concrete evidences is that the appearance of commercial systems such as Siri, SVoice, DARPA, CLASSiC, GSearch etc. Moreover, Spoken Dialogue System is widely believed to be the future direction of software development. In Spoken Dialogue System, users interact to software by using their own voice instead of use their hands, keyboard, and mouse. This paper continuously presents our development of the Spoken Dialogue System on News. Particularly, we propose detailed design such as semantic concepts, semantic frames, slots, and so on for applying Hidden Vector State Model into our Spoken Dialogue System for Spoken Language Understanding.