Báo cáo hóa học: Research Article Exploiting Speech for Automatic TV Delinearization: From Streams to Cross-Media Semantic Navigation

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Báo cáo hóa học: " Research Article Exploiting Speech for Automatic TV Delinearization: From Streams to Cross-Media Semantic Navigation"Hindawi Publishing CorporationEURASIP Journal on Image and Video ProcessingVolume 2011, Article ID 689780, 17 pagesdoi:10.1155/2011/689780Research ArticleExploiting Speech for Automatic TV Delinearization:From Streams to Cross-Media Semantic Navigation Guillaume Gravier,1 Camille Guinaudeau,2 Gw´ nol´ Lecorv´ ,1 and Pascale S´ billot1 ee e e 1 IRISA UMR 6074—CNRS & INSA Rennes, Campus de Beaulieu, F-35042 Rennes Cedex, France 2 INRIA Rennes—Bretagne Atlantique, Campus de Beaulieu, F-35042 Rennes Cedex, France Correspondence should be addressed to Guillaume Gravier, guillaume.gravier@irisa.fr Received 25 June 2010; Revised 27 September 2010; Accepted 20 January 2011 Academic Editor: S. Satoh Copyright © 2011 Guillaume Gravier et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The gradual migration of television from broadcast diffusion to Internet diffusion offers countless possibilities for the generation of rich navigable contents. However, it also raises numerous scientific issues regarding delinearization of TV streams and content enrichment. In this paper, we study how speech can be used at different levels of the delinearization process, using automatic speech transcription and natural language processing (NLP) for the segmentation and characterization of TV programs and for the generation of semantic hyperlinks in videos. Transcript-based video delinearization requires natural language processing techniques robust to transcription peculiarities, such as transcription errors, and to domain and genre differences. We therefore propose to modify classical NLP techniques, initially designed for regular texts, to improve their robustness in the context of TV delinearization. We demonstrate that the modified NLP techniques can efficiently handle various types of TV material and be exploited for program description, for topic segmentation, and for the generation of semantic hyperlinks between multimedia contents. We illustrate the concept of cross-media semantic navigation with a description of our news navigation demonstrator presented during the NEM Summit 2009.1. Introduction available together with links to related contents. The various stages of a typical delinearization chain are illustrated in Figure 1. Clearly, delinearization of TV streams is alreadyTelevision is currently undergoing a deep mutation, gradu-ally shifting from broadcast diffusion to Internet diffusion. a fast-growing trend with the increasing number of catch- up TV sites and video on demand portals. Even if one canThis so-called TV-Internet convergence raises several issues anticipate that Internet diffusion will predominate in a nearwith respect to future services and authoring tools, due to future, we firmly believe that the two diffusion modes willfundamental differences between the two diffusion modes. still coexist for long as they correspond to very differentThe most crucial difference lies in the fact that, by nature, consumption habits. Linear or streaming diffusion, in whichbroadcast diffusion is eminently linear while Internet dif- a continuous TV stream is accessible, is passive while afusion is not, thus permitting features such as navigation, “search-and-browse” enabled diffusion mode requires actionsearch, and personalization. In particular, navigation by from viewers. Such cohabitation is already witnessed with allmeans of links between videos or, in a more general manner, major channels providing catch-up videos on the Internet forbetween multimed ...