Exploiting Speech for Automatic TV Delinearization: From Streams to Cross-Media Semantic Navigation

<p/> <p>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 t...

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Main Authors: Guinaudeau Camille, Gravier Guillaume, Lecorv&#233; Gw&#233;nol&#233;, S&#233;billot Pascale
Format: Article
Language:English
Published: SpringerOpen 2011-01-01
Series:EURASIP Journal on Image and Video Processing
Online Access:http://jivp.eurasipjournals.com/content/2011/689780
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spelling doaj-bdc00999dbe045a0bac369fa0ce0ff552020-11-24T20:43:38ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-51761687-52812011-01-0120111689780Exploiting Speech for Automatic TV Delinearization: From Streams to Cross-Media Semantic NavigationGuinaudeau CamilleGravier GuillaumeLecorv&#233; Gw&#233;nol&#233;S&#233;billot Pascale<p/> <p>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.</p>http://jivp.eurasipjournals.com/content/2011/689780
collection DOAJ
language English
format Article
sources DOAJ
author Guinaudeau Camille
Gravier Guillaume
Lecorv&#233; Gw&#233;nol&#233;
S&#233;billot Pascale
spellingShingle Guinaudeau Camille
Gravier Guillaume
Lecorv&#233; Gw&#233;nol&#233;
S&#233;billot Pascale
Exploiting Speech for Automatic TV Delinearization: From Streams to Cross-Media Semantic Navigation
EURASIP Journal on Image and Video Processing
author_facet Guinaudeau Camille
Gravier Guillaume
Lecorv&#233; Gw&#233;nol&#233;
S&#233;billot Pascale
author_sort Guinaudeau Camille
title Exploiting Speech for Automatic TV Delinearization: From Streams to Cross-Media Semantic Navigation
title_short Exploiting Speech for Automatic TV Delinearization: From Streams to Cross-Media Semantic Navigation
title_full Exploiting Speech for Automatic TV Delinearization: From Streams to Cross-Media Semantic Navigation
title_fullStr Exploiting Speech for Automatic TV Delinearization: From Streams to Cross-Media Semantic Navigation
title_full_unstemmed Exploiting Speech for Automatic TV Delinearization: From Streams to Cross-Media Semantic Navigation
title_sort exploiting speech for automatic tv delinearization: from streams to cross-media semantic navigation
publisher SpringerOpen
series EURASIP Journal on Image and Video Processing
issn 1687-5176
1687-5281
publishDate 2011-01-01
description <p/> <p>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.</p>
url http://jivp.eurasipjournals.com/content/2011/689780
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