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

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... 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.