  To understand a speaker's turn of a conversation, one needs to segment it
into intonational phrases, clean up any speech repairs that might have
occurred, and identify discourse markers. In this paper, we argue that these
problems must be resolved together, and that they must be resolved early in the
processing stream. We put forward a statistical language model that resolves
these problems, does POS tagging, and can be used as the language model of a
speech recognizer. We find that by accounting for the interactions between
these tasks that the performance on each task improves, as does POS tagging and
perplexity.
