2010年
ACCELERATION OF SEQUENCE KERNEL COMPUTATION FOR REAL-TIME SPEAKER IDENTIFICATION
2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
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- 開始ページ
- 1626
- 終了ページ
- 1629
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/ICASSP.2010.5495542
- 出版者・発行元
- IEEE
The sequence kernel has been shown to be a promising kernel function for learning from sequential data such as speech and DNA. However, it is not scalable to massive datasets due to its high computational cost. In this paper, we propose a method of approximating the sequence kernel that is shown to be computationally very efficient. More specifically, we formulate the problem of approximating the sequence kernel as the problem of obtaining a pre-image in a reproducing kernel Hilbert space. The effectiveness of the proposed approximation is demonstrated in text-independent speaker identification experiments with 10 male speakers-our approach provides significant reduction in computation time with limited performance degradation. Based on the proposed method, we develop a real-time kernel-based speaker identification system using Virtual Studio Technology (VST).
- リンク情報
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- DOI
- https://doi.org/10.1109/ICASSP.2010.5495542
- DBLP
- https://dblp.uni-trier.de/rec/conf/icassp/YamadaSWM10
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000287096001153&DestApp=WOS_CPL
- URL
- http://dblp.uni-trier.de/db/conf/icassp/icassp2010.html#conf/icassp/YamadaSWM10
- ID情報
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- DOI : 10.1109/ICASSP.2010.5495542
- ISSN : 1520-6149
- DBLP ID : conf/icassp/YamadaSWM10
- Web of Science ID : WOS:000287096001153