Centroid-aware local discriminative metric learning in speaker verification
Sheng, Kekai1,2; Dong, Weiming1; Li, Wei3; Razik, Joseph4; Huang, Feiyue3; Hu, Baogang1; Weiming Dong
2017-12-01
发表期刊PATTERN RECOGNITION
卷号72期号:72页码:176-185
文章类型Article
摘要We propose a new mechanism to pave the way for efficient learning against class-imbalance and improve representation of identity vector (i-vector) in automatic speaker verification (ASV). The insight is to effectively exploit the inherent structure within ASV corpus - centroid priori. In particular: (1) to ensure learning efficiency against class-imbalance, the centroid-aware balanced boosting sampling is proposed to collect balanced mini-batch; (2) to strengthen local discriminative modeling on the mini-batches, neighborhood component analysis (NCA) and magnet loss (MNL) are adopted in ASV-specific modifications. The integration creates adaptive NCA (AdaNCA) and linear MNL (LMNL). Numerical results show that LMNL is a competitive candidate for low-dimensional projection on i-vector (EER=3.84% on SRE2008, EER=1.81% on SRE2010), enjoying competitive edge over linear discriminant analysis (LDA). AdaNCA (EER=4.03% on SRE2008, EER=2.05% on SRE2010) also performs well. Furthermore, to facilitate the future study on boosting sampling, connections between boosting sampling, hinge loss and data augmentation have been established, which help understand the behavior of boosting sampling further. (C) 2017 Elsevier Ltd. All rights reserved.
关键词Text-independent Asv Centroid-aware Balanced Boosting Sampling Adaptive Neighborhood Component Analysis Linear Magnet
WOS标题词Science & Technology ; Technology
DOI10.1016/j.patcog.2017.07.007
关键词[WOS]RECOGNITION ; CLASSIFICATION ; SPEECH ; END
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China (NSFC)(61573348 ; Institute of Automation Chinese Academy of Sciences (CASIA)-Tencent Youtu Joint Research Project ; 61620106003 ; 61672520)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000411545400013
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/19533
专题模式识别国家重点实验室_多媒体计算与图形学
通讯作者Weiming Dong
作者单位1.Chinese Acad Sci, Inst Automat, LIAMA NLPR, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Tencent Inc, Lab Youtu, Shanghai, Peoples R China
4.Univ Toulon & Var, Lab LSIS, Toulon, France
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GB/T 7714
Sheng, Kekai,Dong, Weiming,Li, Wei,et al. Centroid-aware local discriminative metric learning in speaker verification[J]. PATTERN RECOGNITION,2017,72(72):176-185.
APA Sheng, Kekai.,Dong, Weiming.,Li, Wei.,Razik, Joseph.,Huang, Feiyue.,...&Weiming Dong.(2017).Centroid-aware local discriminative metric learning in speaker verification.PATTERN RECOGNITION,72(72),176-185.
MLA Sheng, Kekai,et al."Centroid-aware local discriminative metric learning in speaker verification".PATTERN RECOGNITION 72.72(2017):176-185.
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