Information Retrieval from Unsegmented Broadcast News Audio

International audienceThis paper describes a system for retrieving relevant portions of broadcast news shows starting with only the audio data. A novel method of automatically detecting and removing commercials is presented and shown to increase the performance of the system while also reducing the computational effort required. A sophisticated large vocabulary speech recogniser which produces high-quality transcriptions of the audio and a window-based retrieval system with post-retrieval merging are also described. Results are presented using the 1999 TREC-8 Spoken Document Retrieval data for the task where no story boundaries are known. Experiments investigating the effectiveness of all aspects of the system are described, and the relative benefits of automatically eliminating commercials, enforcing broadcast structure during retrieval, using relevance feedback, changing retrieval parameters and merging during post-processing are shown. An Average Precision of 46.8%, when duplicates are scored as irrelevant, is shown to be achievable using this system, with the corresponding word error rate of the recogniser being 20.5%

Ad hoc, Cross-language and Spoken Document Information Retrieval at IBM

Introduction The Natural Language Systems group at IBM participated in three tracks at TREC-8: ad hoc, SDR and cross-language. Our SDR and ad hoc participation included experiments involving query expansion and clustering-induced document reranking. Our CLIR participation involved both the French and English queries and included experiments with the merging strategy. 2 Ad Hoc Track In the TREC-8 ad hoc experiments we used a two-pass approach, in which the top documents, as ranked by the Okapi formula [1], were used to construct expanded queries, which were then used to compute the final scores. We also experimented with applying a clustering algorithm to obtain a more reliable list of passages for query expansion. The data pre-processing agorithm was similar to the one we used in our previous TREC participations [2], [3]. It consisted of a decision tree based tokenizer, part-of-speech tagger [4] and a morphological analyzer. Filler query prefixes were remov