We investigate genre effects on the task of automatic sentence segmentation, focusing on two important domains - broadcast news (BN) and broadcast conversation (BC). We employ an HMM model based on textual and prosodic information and analyze differences in segmentation accuracy and feature usage between the two genres using both manual and automatic speech transcripts. Experiments are evaluated using Czech broadcast corpora annotated for sentence-like units (SUs). Prosodic features capture information about pause, duration, pitch, and energy patterns. Textual knowledge sources include words, part-of-speech, and automatically induced classes. We also analyze effects of using additional textual data that is not annotated for SUs. Feature analysis reveals significant differences in both textual and prosodic feature usage patterns between the two genres. The analysis is important for building automatic understanding systems when limited matched-genre data are available, or for designing eventual genre-independent systems.
['Jachym Kolar', 'Yang Liu', 'Elizabeth Shriberg']
Genre effects on automatic sentence segmentation of speech: A comparison of broadcast news and broadcast conversations