The THISL SDR system at TREC-8

This paper describes the participation of the THISL group at the TREC-8 Spoken Document Retrieval (SDR) track. The THISL SDR system consists of the realtime version of the ABBOT large vocabulary speech recognition system and the THISLIR text retrieval system. The TREC-8 evaluation assessed SDR performance on a corpus of 500 hours of broadcast news material collected over a five month period. The main test condition involved retrieval of stories defined by manual segmentation of the corpus in which non-news material, such as commercials, were excluded. An optional test condition required required retrieval of the same stories from the unsegmented audio stream. The THISL SDR system participated at both test conditions. The results show that a system such as THISL can produce respectable information retrieval performance on a realistically-sized corpus of unsegmented audio material

Phonetic Searching

An improved method and apparatus is disclosed which uses probabilistic techniques to map an input search string with a prestored audio file, and recognize certain portions of a search string phonetically. An improved interface is disclosed which permits users to input search strings, linguistics, phonetics, or a combination of both, and also allows logic functions to be specified by indicating how far separated specific phonemes are in time.Georgia Tech Research Corporatio

Phonetic Searching

An improved method and apparatus is disclosed which uses probabilistic techniques to map an input search string with a prestored audio file, and recognize certain portions of a search string phonetically. An improved interface is disclosed which permits users to input search strings, linguistics, phonetics, or a combination of both, and also allows logic functions to be specified by indicating how far separated specific phonemes are in time.Georgia Tech Research Corporatio

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%