Edinburgh Research Explorer

Maximum entropy segmentation of broadcast news

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationProceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2005
Subtitle of host publicationICASSP 2005
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1029-1032
Number of pages4
ISBN (Print)0-7803-8874-7
DOIs
Publication statusPublished - 2005
Event2005 IEEE International Conference on Acoustics, Speech and Signal Processing - Pennsylvania Convention Center/Marriott Hotel, Philadelphia, PA, United States
Duration: 18 Mar 200523 Mar 2005

Conference

Conference2005 IEEE International Conference on Acoustics, Speech and Signal Processing
CountryUnited States
CityPhiladelphia, PA
Period18/03/0523/03/05

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.

Event

2005 IEEE International Conference on Acoustics, Speech and Signal Processing

18/03/0523/03/05

Philadelphia, PA, United States

Event: Conference

Download statistics

No data available

ID: 27399833