Using term clouds to represent segment-level semantic content of podcasts
Fuller, Marguerite and Tsagkias, Manos and Newman, Eamonn and Besser, Jana and Larson, Martha and Jones, Gareth J.F. and de Rijke, Maarten (2008) Using term clouds to represent segment-level semantic content of podcasts. In: the Workshop on Searching Spontaneous Conversational Speech at Thirty-First Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), 24 July 2008, Singapore.
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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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