Recently various data-driven spoken language technologies have been applied to spoken dialog system development. However, high cost of maintaining the spoken dialog systems is one of the biggest challenges. In addition, a fixed corpus collected by human is never enough to cover diverse real user's utterances. The concept of a daydreaming dialog system can solve the problem by making the system learn from previous human-machine dialogs. This paper introduces D3 (Daydreaming Dialog system Development) toolkit, which is a back-end support toolkit for the development of the daydreaming spoken dialog systems. For reducing human efforts, D3 toolkit generates new utterances with semantic annotation and new knowledge by analyzing the usage log file. The new added corpus is determined by verifying proper candidates using semi-automatic methods. The augmented corpus is used for building improved models and self-evolution of the dialog system is possible by replacing the old models. We implemented the D3 toolkit using web-based technologies to provide a familiar environment to non-expert end-users.
['Donghyeon Lee', 'Kyungduk Kim', 'Cheongjae Lee', 'Junhwi Choi', 'Gary Geunbae Lee']
D3 toolkit: a development toolkit for daydreaming spoken dialog systems