CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
An iVector Extractor Using Pre-trained Neural Networks for Speaker Verification
Shanshan, Zhang; Rong, Zheng; Bo, Xu
2014
Conference NameInternational Symposium on Chinese Spoken Language Processing
Source PublicationInternational Symposium on Chinese Spoken Language Processing
Conference Date2014
Conference PlaceSingapore
Abstract
; The iVector representation of speech utterances is currently
widely used in speaker and language recognition tasks. In this
paper, an iVector extractor using pre-trained neural networks
is proposed for speaker verification. It can be viewed as
an alternative to the classical total variability approach. In
the proposed system, a neural network with bottleneck layer
is trained with speaker labeled utterances, then we utilize
the bottleneck features of the network to represent the input
utterance. As a new iVector representation, it shows comparable
performance with the state-of-the-art Total Variability Model
(TVM) based iVector extraction system on NIST 2008 SRE.
We further achieve a 10% reduction in equal error rates with
combination of the proposed extraction system and the TVM
system.
KeywordIvector Extractor Bottleneck Feature Speaker Verification
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11806
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorShanshan, Zhang
AffiliationInteractive Digital Media Technology Research Center Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shanshan, Zhang,Rong, Zheng,Bo, Xu. An iVector Extractor Using Pre-trained Neural Networks for Speaker Verification[C],2014.
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