The Fraunhofer IAIS AudioMining system for vocabulary independent spoken term detection is able to provide automatic speech recognition (ASR) transcripts for audio-visual data. These transcripts can be used to search for information, e.g., in audio-visual archives. We experienced difficulties in the process of browsing for desired content when only these transcripts are given, especially since they are erroneous due to the ASR. Hence, we propose a lightweight and fast algorithm to retrieve keywords and tag-clouds from ASR transcripts to support content browsing. In contrast to similar algorithms, it calculates keywords ad-hoc and query-dependent while searching on a corresponding index. The proposed algorithm takes into account the relation between keywords and the search query, text weighting and linguistic constraints. For visualization we chose a scalable tag-cloud. An evaluation yielded comparable precision-recall scores and promising usability ratings.
Bibliographic reference. Tschöpel, Sebastian / Schneider, Daniel (2010): "A lightweight keyword and tag-cloud retrieval algorithm for automatic speech recognition transcripts", In INTERSPEECH-2010, 1277-1280.