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 -- for both true and automatically recognized words in news speech. The prosodic model achieves comparable performance with significantly less training data, and requires no hand-labeling of prosodic events. Across tasks and corpora, we obtain a significant improvement over word-only models using a probabilistic combination of prosodic and lexical information. Inspection reveals that the prosodic models capture language-independent boundary indicators described in the literature. Finally, cue usage is task and corpus dependent. For example, pause and pitch features are highly informative for segmenting news speech, whereas pause, duration and word-based cues dominate for natural conversation.Comment: 30 pages, 9 figures. To appear in Speech Communication 32(1-2), Special Issue on Accessing Information in Spoken Audio, September 200

Problem spotting in human-machine interaction

In human-human communication, dialogue participants are con-tinuously sending and receiving signals on the status of the inform-ation being exchanged. We claim that if spoken dialogue systems were able to detect such cues and change their strategy accordingly, the interaction between user and systemwould improve. Therefore, the goals of the present study are as follows: (i) to find out which positive and negative cues people actually use in human-machine interaction in response to explicit and implicit verification questions and (ii) to see which (combinations of) cues have the best predictive potential for spotting the presence or absence of problems. It was found that subjects systematically use negative/marked cues (more words, marked word order, more repetitions and corrections, less new information etc.) when there are communication problems. Using precision and recall matrices it was found that various combinations of cues are accurate problem spotters. This kind of information may turn out to be highly relevant for spoken dia-logue systems, e.g., by providing quantitative criteria for changing the dialogue strategy or speech recognition engine