Automatic Segmentation of Multiparty Dialogue 
 In this paper, we investigate the problem of automatically predicting segment boundaries in spoken multiparty dialogue . We extend prior work in two ways. We first apply approaches that have been proposed for predicting top-level topic shifts to the problem of identifying subtopic boundaries . We then explore the impact on performance of using ASR output as opposed to human transcription . Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries , the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries , the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues , such as cue phrases and overlapping speech , are better indicators for the top-level prediction task. We also find that the transcription errors inevitable in ASR output have a negative impact on models that combine lexical-cohesion and conversational features , but do not change the general preference of approach for the two tasks.  