| dc.contributor.author | Christensen, Heidi | en |
| dc.contributor.author | Kolluru, BalaKrishna | en |
| dc.contributor.author | Gotoh, Yoshihiko | en |
| dc.contributor.author | Renals, Steve | en |
| dc.date.accessioned | 2006-05-09T14:36:48Z | |
| dc.date.available | 2006-05-09T14:36:48Z | |
| dc.date.issued | 2005 | |
| dc.identifier.citation | In Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-05), Philadelphia, PA, USA, March 2005. | |
| dc.identifier.uri | http://hdl.handle.net/1842/948 | |
| dc.description.abstract | This paper presents an automatic system for structuring and preparing a news broadcast for applications such as speech summarization, browsing, archiving and information retrieval. This process comprises transcribing the audio using an automatic speech recognizer and subsequently segmenting the text into utterances and topics. A maximum entropy approach is used to build statistical models for both utterance and topic segmentation. The experimental work addresses the effect on performance of the topic boundary detector of three factors: the information sources used, the quality of the ASR transcripts, and the quality of the utterance boundary detector. The results show that the topic segmentation is not affected severely by transcripts errors, whereas errors in the utterance segmentation are more devastating. | en |
| dc.format.extent | 84359 bytes | en |
| dc.format.extent | 117020 bytes | en |
| dc.format.mimetype | application/octet-stream | en |
| dc.format.mimetype | application/pdf | en |
| dc.language.iso | en | |
| dc.publisher | IEEE Signal Processing Society Press. | en |
| dc.title | Maximum entropy segmentation of broadcast news | en |
| dc.type | Conference Paper | en |
