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...
Main Authors: | , , , |
---|---|
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 |
id |
doaj-bdc00999dbe045a0bac369fa0ce0ff55 |
---|---|
record_format |
Article |
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 GuillaumeLecorvé GwénoléSébillot 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 Lecorvé Gwénolé Sébillot Pascale |
spellingShingle |
Guinaudeau Camille Gravier Guillaume Lecorvé Gwénolé Sébillot 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 Lecorvé Gwénolé Sébillot 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 |
work_keys_str_mv |
AT guinaudeaucamille exploitingspeechforautomatictvdelinearizationfromstreamstocrossmediasemanticnavigation AT gravierguillaume exploitingspeechforautomatictvdelinearizationfromstreamstocrossmediasemanticnavigation AT lecorv233gw233nol233 exploitingspeechforautomatictvdelinearizationfromstreamstocrossmediasemanticnavigation AT s233billotpascale exploitingspeechforautomatictvdelinearizationfromstreamstocrossmediasemanticnavigation |
_version_ |
1716819324285485056 |