Castsearch - Context Based Spoken Document Retrieval

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2007




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The paper describes our work on the development of a system for retrieval of relevant stories from broadcast news. The system utilizes a combination of audio processing and text mining. The audio processing consists of a segmentation step that partitions the audio into speech and music. The speech is further segmented into speaker segments and then transcribed using an automatic speech recognition system, to yield text input for clustering using non-negative matrix factorization (NMF). We find semantic topics that are used to evaluate the performance for topic detection. Based on these topics we show that a novel query expansion can be performed to return more intelligent search results. We also show that the query expansion helps overcome errors of the automatic transcription
Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing, 2007. ICASSP 2007.
Publication date2007
ISBN (print)1-4244-0727-3
StatePublished - 2007
Event32nd IEEE International Conference on Acoustics, Speech and Signal Processing 2007 - Honolulu, HI, United States


Conference32nd IEEE International Conference on Acoustics, Speech and Signal Processing 2007
CountryUnited States
CityHonolulu, HI

Bibliographical note

Copyright: 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

CitationsWeb of Science® Times Cited: No match on DOI


  • Nonnegative Matrix Factorization, Audio Retrieval, Text Mining, Document Clustering
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