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faculty: "FNWI" and publication year: "2008"
| Authors||M. Fuller, E. Tsagkias, E. Newman, J. Besser, M. Larson, G.J.F. Jones, M. de Rijke|
|Title||Using term clouds to represent segment-level semantic content of podcasts|
|Book/source title||Proceedings of the ACM SIGIR Workshop 'Searching Spontaneous Conversational Speech'|
|Authors/Editors||J. Köhler, M. Larson, F.M.G. de Jong, W. Kraaij, R.J.F. Ordelman|
|Publisher||Centre for Telematics and Information Technology (CTIT)|
|Faculty||Faculty of Science|
|Institute/dept.||FNWI: Informatics Institute (II)|
|Abstract||Spoken audio, like any time-continuous medium, is notoriously difficult to browse or skim without support of an interface providing semantically annotated jump points to signal the user where to listen in. Creation of time-aligned metadata by human annotators is prohibitively expensive, motivating the investigation of representations of segment-level semantic content based on transcripts generated by automatic speech recognition (ASR). This paper examines the feasibility of using term clouds to provide users with a structured representation of the semantic content of podcast episodes. Podcast episodes are visualized as a series of sub-episode segments, each represented by a term cloud derived from a transcript generated by automatic speech recognition (ASR). Quality of segment-level term clouds is measured quantitatively and their utility is investigated using a small-scale user study based on human labeled segment boundaries. Since the segment-level clouds generated from ASR-transcripts prove useful, we examine an adaptation of text tiling techniques to speech in order to be able to generate segments as part of a completely automated indexing and structuring system for browsing of spoken audio. Results demonstrate that the segments generated are comparable with human selected segment boundaries.|
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