Prosody-Based Automatic Segmentation of Speech into Sentences and Topics

A crucial step in processing speech audio data for information extraction, topic detection, or browsing/playback is to segment the input into sentence and topic units. Speech segmentation is challenging, since the cues typically present for segmenting text (headers, paragraphs, punctuation) are absent in spoken language. We investigate the use of prosody (information gleaned from the timing and melody of speech) for these tasks. Using decision tree and hidden Markov modeling techniques, we combine prosodic cues with word-based approaches, and evaluate performance on two speech corpora, Broadcast News and Switchboard. Results show that the prosodic model alone performs on par with, or better than, word-based statistical language models

• Type: Journal
• Year: 2000
• Where: CORR
• Authors: Elizabeth Shriberg, Andreas Stolcke, Dilek Z. Hakkani-Tür, Gökhan Tür
• Keywords: CORR 2000, Education, Prosodic, Prosodic Cues, Prosodic Models