Extracting clauses for spoken language understanding in conversational systems

Spontaneous human utterances in the context of human-human and human-machine dialogs are rampant with dysfluencies, and speech repairs. Furthermore, when recognized using a speech recognizer, these utterances produce a sequence of words with no identification of clausal units. Such long strings of words combined with speech errors pose a difficult problem for spoken language parsing and understanding. In this paper, we address the issue of editing speech repairs as well as segmenting user utterances into clause units with a view of parsing and understanding spoken language utterances. We present generative and discriminative models for this task and present evaluation results on the human-human conversations obtained from the Switchboard corpus. 1