Evaluation of the usefulness of spoken term detection in an electronic note-taking support system

Chifuyu, Yonekura; Furuya, Yuto; Natori, Satoshi; Nishizaki, Hiromitsu; Sekiguchi, Y.

doi:10.1109/apsipa.2013.6694145

Cited by 2 publications

(1 citation statement)

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“…STD enables us to search for a specified word from recorded speech, and its effectiveness has been demonstrated through an electronic note-taking system [1]. However, STD is difficult to use when searching for terms within a vocabulary-free framework because search terms are not known by the STD process prior to implementing a large vocabulary continuous speech recognition (LVCSR) system.…”

Section: Introductionmentioning

confidence: 99%

Spoken Term Detection Using SVM-Based Classifier Trained with Pre-Indexed Keywords

Domoto

Utsuro

Sawada

et al. 2016

IEICE Trans. Inf. & Syst.

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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.

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Section: Introductionmentioning

confidence: 99%