Electronic media, indexing, Digital libraries, Speech recognition, Semantic web, Domain ontologies


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.

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 (WCN). By the use of AI error-training procedures, optimization is sought for each index item.

Research limitations/Implications - Validation and implementation of this approach within the field of digital libraries still remain under development, but rapid developments in technology and research show rich promise for automated speech indexing – within the bounds of our conceptual model or of one very similar to it.

Originality/value – Huge masses of spoken data, unmanageable for a human indexer, can prospectively find semantically sorted and prioritized indexing – not transcription, but generated metadata – automatically, quickly and accurately.

Original Publication Citation

Library Hi Tech 32 (2): 206-218

Document Type

Peer-Reviewed Article

Publication Date


Permanent URL


Emerald Publishing




Harold B. Lee Library

University Standing at Time of Publication

Full Professor