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A lightweight keyword and tag-cloud retrieval algorithm for automatic speech recognition transcripts

Presentation held at the 11th Annual International Conference on Spoken Language Processing, (INTERSPEECH), Makuhari, Japan, 26.-30.09.2010
: Tschöpel, S.; Schneider, D.

Preprint urn:nbn:de:0011-n-1345342 (102 KByte PDF)
MD5 Fingerprint: 295f7ba4660bc7da5ae68a4a3b9a74dc
Erstellt am: 8.7.2010

2010, 4 S.
International Conference on Spoken Language Processing (INTERSPEECH) <11, 2010, Makuhari>
Vortrag, Elektronische Publikation
Fraunhofer IAIS ()
keyword extraction; tag-cloud; automatic speech recognition; content browsing

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.