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

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Exploiting Speech for Automatic TV Delinearization: From Streams to Cross-Media Semantic Navigation | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2011 Article ID 689780 17 pages doi 2011 689780 Research Article Exploiting Speech for Automatic TV Delinearization From Streams to Cross-Media Semantic Navigation Guillaume Gravier 1 Camille Guinaudeau 2 Gwenole Lecorve 1 and Pascale Sebillot1 1IRISA UMR 6074 CNRS INSA Rennes Campus de Beaulieu F-35042 Rennes Cedex France 2INRIA Rennes Bretagne Atlantique Campus de Beaulieu F-35042 Rennes Cedex France Correspondence should be addressed to Guillaume Gravier 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 .