Semantic ontologies for multimedia indexing (SOMI): Application in the e-library domain

Purpose – The overwhelming speed and scale of digital media production greatly outpace conventional indexing methods by humans. The management of Big Data for e-library speech resources requires an automated metadata solution. The paper aims to discuss these issues. Design/methodology/approach – A conceptual model called semantic ontologies for multimedia indexing (SOMI) allows for assembly of the speech objects, encapsulation of semantic associations between phonic units and the definition of indexing techniques designed to invoke and maximize the semantic ontologies for indexing. A literature review and architectural overview are followed by evaluation techniques and a conclusion. Findings – This approach is only possible because of recent innovations in automated speech recognition. The introduction of semantic keyword spotting allows for indexing models that disambiguate and prioritize meaning using probability algorithms within a word confusion network. By the use of AI error-training procedures, opt...

[1]  Bendib Issam,et al.  Approaches for the detection of the keywords in spoken documents application for the field of e-libraries , 2012, ICONIP 2012.

[2]  Hermann Ney,et al.  Confidence measures for large vocabulary continuous speech recognition , 2001, IEEE Trans. Speech Audio Process..

[3]  Parul Gupta,et al.  Context based Indexing in Search Engines using Ontology , 2010 .

[4]  Martha Larson,et al.  Sub-word-based language models for speech recognition : implications for spoken document retrieval , 2001 .

[5]  Alex Acero,et al.  Soft indexing of speech content for search in spoken documents , 2007, Comput. Speech Lang..

[6]  Douglas D. O'Shaughnessy,et al.  Lexical fillers for task-independent-training based keyword spotting and detection of new words , 1995, EUROSPEECH.

[7]  Beth Logan,et al.  Word and sub-word indexing approaches for reducing the effects of OOV queries on spoken audio , 2002 .

[8]  Richard M. Stern,et al.  Integration of continuous speech recognition and information retrieval for mutually optimal performance , 1999 .

[9]  Karen Spärck Jones,et al.  Retrieving spoken documents by combining multiple index sources , 1996, SIGIR '96.

[10]  Daben Liu,et al.  Speech and language technologies for audio indexing and retrieval , 2000, Proceedings of the IEEE.

[12]  Haïfa Zargayouna,et al.  Mesure de similarité dans une ontologie pour l'indexation sémantique de documents XML , 2004 .

[13]  David Carmel,et al.  Spoken document retrieval from call-center conversations , 2006, SIGIR.

[14]  Mikko Kurimo,et al.  Indexing confusion networks for morph-based spoken document retrieval , 2007, SIGIR.

[15]  Dragutin Petkovic,et al.  Phonetic confusion matrix based spoken document retrieval , 2000, SIGIR '00.