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Query: faculty: "FNWI" and publication year: "2008"

AuthorsM. Fuller, E. Tsagkias, E. Newman, J. Besser, M. Larson, G.J.F. Jones, M. de Rijke
TitleUsing term clouds to represent segment-level semantic content of podcasts
Book/source titleProceedings of the ACM SIGIR Workshop 'Searching Spontaneous Conversational Speech'
Authors/EditorsJ. Köhler, M. Larson, F.M.G. de Jong, W. Kraaij, R.J.F. Ordelman
PublisherCentre for Telematics and Information Technology (CTIT)
PlaceEnschede
Year2008
Pages12-19
ISBN9789036526975
FacultyFaculty of Science
Institute/dept.FNWI: Informatics Institute (II)
AbstractSpoken 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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