An Automated Tagging Approach to Improve Search and Retrieval in a Radio Archive

Abstract

Although a manual process guarantees accurate tagging of archive material, it is very time-consuming. Hence, not all media content in a big broadcast archive can be annotated sufficiently. A more automated tagging process could reduce the amount of unannotated content. This paper describes how speech technology is applied to archived content of one year of the Flemish Radio and Television Network's Radio 1 Dutch news to address the issue described. Different options are discussed and an automated approach is suggested. Techniques such as speech recognition, keyword spotting, and keyword extraction are combined to generate automatic annotations. A search engine prototype was implemented to assess the findability of the radio content. Results from the prototype show that the proposed automated approach can improve annotation and search efficiency significantly while still maintaining high precision. The results and lessons learned and presented are not only valuable to archivists, but to other professional users such as journalists and even end users.