Re-Ranking Approach of Spoken Term Detection Using Conditional Random Fields-Based Triphone Detection
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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
抄録
<p>This study proposes a two-pass spoken term detection (STD) method. The first pass uses a phoneme-based dynamic time warping (DTW)-based STD, and the second pass recomputes detection scores produced by the first pass using conditional random fields (CRF)-based triphone detectors. In the second-pass, we treat STD as a sequence labeling problem. We use CRF-based triphone detection models based on features generated from multiple types of phoneme-based transcriptions. The models train recognition error patterns such as phoneme-to-phoneme confusions in the CRF framework. Consequently, the models can detect a triphone comprising a query term with a detection probability. In the experimental evaluation of two types of test collections, the CRF-based approach worked well in the re-ranking process for the DTW-based detections. CRF-based re-ranking showed 2.1% and 2.0% absolute improvements in F-measure for each of the two test collections.</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), 2518-2527, 2016
一般社団法人 電子情報通信学会
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キーワード
詳細情報
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- CRID
- 1390282679356531328
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- NII論文ID
- 130005598239
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- ISSN
- 17451361
- 09168532
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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
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可