Spoken Term Detection Using SVM-Based Classifier Trained with Pre-Indexed Keywords
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- DOMOTO Kentaro
- Graduate School of Systems and Information Engineering, University of Tsukuba
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- UTSURO Takehito
- Graduate School of Systems and Information Engineering, University of Tsukuba
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- SAWADA Naoki
- Integrated Graduate School of Medicine, Engineering, and Agricultural Sciences, University of Yamanashi
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- NISHIZAKI Hiromitsu
- Integrated Graduate School of Medicine, Engineering, and Agricultural Sciences, University of Yamanashi
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<p>This 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 pre-index 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 two-stage 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.</p>
収録刊行物
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E99.D (10), 2528-2538, 2016
一般社団法人 電子情報通信学会
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詳細情報
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- CRID
- 1390282679356529024
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- NII論文ID
- 130005598236
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- NII書誌ID
- AA10826272
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- ISSN
- 17451361
- 09168532
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- HANDLE
- 2241/00144992
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- IRDB
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可