Selection of best match keyword using spoken term detection for spoken document indexing

Abstract

This paper presents a novel keyword selection-based spoken document-indexing framework that selects the best match keyword from query candidates using spoken term detection (STD) for spoken document retrieval. Our method comprises creating a keyword set including keywords that are likely to be in a spoken document. Next, an STD is conducted for all the keywords as query terms for STD; then, the detection result, a set of each keyword and its detection intervals in the spoken document, is obtained. For the keywords that have competitive intervals, we rank them based on the matching cost of STD and select the best one with the longest duration among competitive detections. This is the final output of STD process and serves as an index word for the spoken document. The proposed framework was evaluated on lecture speeches as spoken documents in an STD task. The results show that our framework was quite effective for preventing false detection errors and in annotating keyword indices to spoken documents.