Multimodal Indexing of Multilingual News Video | Zendy

Hiranmay Ghosh | Zendy; Sunil Kumar Kopparapu | Zendy; Tanushyam Chattopadhyay | Zendy; Ashish Khare | Zendy; Sujal Subhash Wattamwar | Zendy; Amarendra Gorai | Zendy; Meghna Pandharipande | Zendy

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Multimodal Indexing of Multilingual News Video

Author(s) -

Hiranmay Ghosh,

Sunil Kumar Kopparapu,

Tanushyam Chattopadhyay,

Ashish Khare,

Sujal Subhash Wattamwar,

Amarendra Gorai,

Meghna Pandharipande

Publication year - 2010

Publication title -

international journal of digital multimedia broadcasting

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.164

H-Index - 17

eISSN - 1687-7586

pISSN - 1687-7578

DOI - 10.1155/2010/486487

Subject(s) - computer science , search engine indexing , focus (optics) , rss , domain (mathematical analysis) , information retrieval , german , set (abstract data type) , analytics , natural language processing , world wide web , data science , linguistics , philosophy , physics , mathematics , optics , programming language , mathematical analysis

The problems associated with automatic analysis of news telecasts are more severe in a country like India, where there are many national and regional language channels, besides English. In this paper, we present a framework for multimodal analysis of multilingual news telecasts, which can be augmented with tools and techniques for specific news analytics tasks. Further, we focus on a set of techniques for automatic indexing of the news stories based on keywords spotted in speech as well as on the visuals of contemporary and domain interest. English keywords are derived from RSS feed and converted to Indian language equivalents for detection in speech and on ticker texts. Restricting the keyword list to a manageable number results in drastic improvement in indexing performance. We present illustrative examples and detailed experimental results to substantiate our claim

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