Three phase verification for spoken dialog clarification

Abstract

Spoken dialog tasks incur many errors including speech recognition errors, understanding errors, and even dialog management errors. These errors create a big gap between user's will and the system's understanding, and eventually result in a misinterpretation. To fill in the gap, people in human-to-human dialog try to clarify the major causes of the misunderstanding and selectively correct them. This paper presents a method for applying the human's clarification techniques to human-machine spoken dialog systems. To increase the error detection precision and error recovery efficiency for the clarification dialogs, error detection phase is organized into three systematic phases and a clarification expert is devised for recovering the errors using the three phase verification. The experiment results demonstrate that the three phase verification could effectively catch the word and utterance-level errors in order to increase the SLU (spoken language understanding) performance and the clarification experts can actually increase the dialog success rate and the dialog efficiency.

Clarification Question Generation for Speech Recognition Error Recovery Using Monolingual SMT

“…These works are task oriented, where CD is aimed at restricting human-machine interaction to the task domain, and out-of-domain input will be treated as misunderstanding. The second one is specific task based clarification, including works of Lewis et.al [4], Misu et al [5] and Sangkeun et al [6]. These works require that the task knowledge has a well defined structure, or it can be represented into a certain form.…”