CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Evaluation of the deep nonlinear metric learning based speaker identification on the large scale of voiceprint corpus
Feng Yong; Cai Xinyuan; Ji Ruifang
2016-10
Conference NameISCSLP 2016
Source PublicationISCSLP 2016
Conference Date2016.10.17-2016.10.20
Conference PlaceTian Jin, China
AbstractSpeaker recognition is a valuable biometric recognition technology. Recently, with the development of deep learning, many deep network based speaker recognition algorithms are proposed. However, the evaluations on the speaker recognition algorithms are always performed on a small or middle scale of voiceprint corpus. There are few evaluation reports of speaker recognition on a large scale of voiceprint corpus. In this paper, we try to introduce a deep nonlinear metric learning based speaker identification algorithm and construct a larger scale of voiceprint corpus consisting of about 440 thousand people. We perform the evaluation of the deep nonlinear metric learning based method on the large scale corpus and give the recognition rate results in different scales. The evaluation results prove that, the performance is surely decreased when the scale of the corpus is increased. But for the scale of 400 thousand, the recognition rate of Top 50 is stable at about 95%, which means that it could be used in some real applications.
KeywordSpeaker Identification Deep Nonlinear Metric Learning Large Scale Of Voiceprint Corpus
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12488
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorCai Xinyuan
AffiliationSchool of Automation, Chongqing University
Recommended Citation
GB/T 7714
Feng Yong,Cai Xinyuan,Ji Ruifang. Evaluation of the deep nonlinear metric learning based speaker identification on the large scale of voiceprint corpus[C],2016.
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