Improved spoken document retrieval by exploring extra acoustic and linguistic cues

In this paper, we explored the use of various extra information to improve the performance of spoken document retrieval (SDR). From the speech recognition perspective, we incorporated the acoustic stress and word confusion information into the audio indexing. From the linguistic perspective, we applied the partof-speech information in both the audio indexing and the query representation. From the information retrieval perspective, we integrated techniques such as the query expansion by word associations and the blind relevance feedback into the retrieval process. The SDR experiments were based on the Topic Detection and Tracking Corpora (TDT-2 and TDT-3). We used the Chinese newswire text stories as query exemplars and the Mandarin Chinese audio news stories as the spoken documents. With all the above acoustic and linguistic cues applied, the average precision was improved from 0.5122 to 0.6312 for the TDT-2 collection and from 0.6216 to 0.7172 for the TDT-3 collection.