Ensemble based speaker recognition using unsupervised data selection

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題名:	Ensemble based speaker recognition using unsupervised data selection
作者:	王家慶;Huang, Chien-Lin;Wang, Jia-Ching;Ma, Bin
貢獻者:	資訊電機學院資訊工程學系
關鍵詞:	Acoustics;Adaptation;Classifiers;Clustering;Local optimization;Machine learning;Neural networks;Original Paper;Phonetics;Semantics;Speech;Speech recognition
日期:	2016-05-10
上傳時間:	2026-04-23 13:39:00 (UTC+8)
出版者:	Cambridge University Press;Cambridge, UK: Cambridge University Press
摘要:	摘要： This paper presents an ensemble-based speaker recognition using unsupervised data selection. Ensemble learning is a type of machine learning that applies a combination of several weak learners to achieve an improved performance than a single learner. A speech utterance is divided into several subsets based on its acoustic characteristics using unsupervised data selection methods. The ensemble classifiers are then trained with these non-overlapping subsets of speech data to improve the recognition accuracy. This new approach has two advantages. First, without any auxiliary information, we use ensemble classifiers based on unsupervised data selection to make use of different acoustic characteristics of speech data. Second, in ensemble classifiers, we apply the divide-and-conquer strategy to avoid a local optimization in the training of a single classifier. Our experiments on the 2010 and 2008 NIST Speaker Recognition Evaluation datasets show that using ensemble classifiers yields a significant performance gain. 其他題名： APSIPA Transactions on Signal and Information Processing 出版者： Cambridge, UK: Cambridge University Press 出版日期： 2016-01-01 出處： APSIPA transactions on signal and information processing, 2016-01, Vol.5 (1) 資源來源： Alma/SFX Local Collection 版權： Copyright © The Authors, 2016 版權： Copyright © The Authors, 2016 This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is included and the original work is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use. 識別號： ISSN: 2048-7703 識別號： EISSN: 2048-7703 識別號： DOI: 10.1017/ATSIP.2016.10
顯示於類別:	[資訊工程學系] 期刊論文

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