SUMMARYThis study presents a two-stage spoken term detection (STD) method that uses the same STD engine twice and a support vector machine (SVM)-based classifier to verify detected terms from the STD engine's output. In a front-end process, the STD engine is used to preindex target spoken documents from a keyword list built from an automatic speech recognition result. The STD result includes a set of keywords and their detection intervals (positions) in the spoken documents. For keywords having competitive intervals, we rank them based on the STD matching cost and select the one having the longest duration among competitive detections. The selected keywords are registered in the pre-index. They are then used to train an SVM-based classifier. In a query term search process, a query term is searched by the same STD engine, and the output candidates are verified by the SVM-based classifier. Our proposed twostage STD method with pre-indexing was evaluated using the NTCIR-10 SpokenDoc-2 STD task and it drastically outperformed the traditional STD method based on dynamic time warping and a confusion network-based index.
This paper presents a novel spoken document indexing framework for Spoken Term Detection (STD). Our proposed method utilizes an STD method for making an index from keywords collected from outputs from automatic speech recognition systems. The STD method is conducted for all the keywords as query terms; 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 real 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.
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