| 基于DNN处理的鲁棒性I-Vector说话人识别算法 |
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| 引用本文： | 王 昕,张洪冉. 基于DNN处理的鲁棒性I-Vector说话人识别算法[J]. 计算机工程与应用, 2018, 54(22): 167-172. DOI: 10.3778/j.issn.1002-8331.1707-0207 |
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| 作者姓名： | 王 昕 张洪冉 |
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| 作者单位： | 南京邮电大学 通信与信息工程学院，南京 210003 |
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| 摘 要： | 提出了一种将基于深度神经网络（Deep Neural Network，DNN）特征映射的回归分析模型应用到身份认证矢量（identity vector，i-vector）/概率线性判别分析（Probabilistic Linear Discriminant Analysis，PLDA）说话人系统模型中的方法。DNN通过拟合含噪语音和纯净语音i-vector之间的非线性函数关系，得到纯净语音i-vector的近似表征，达到降低噪声对系统性能影响的目的。在TIMIT数据集上的实验验证了该方法的可行性和有效性。
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| 关 键 词： | 说话人识别 深度神经网络 i-vector |
Robust i-vector speaker recognition method based on DNN processing |
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| WANG Xin,ZHANG Hongran. Robust i-vector speaker recognition method based on DNN processing[J]. Computer Engineering and Applications, 2018, 54(22): 167-172. DOI: 10.3778/j.issn.1002-8331.1707-0207 |
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| Authors: | WANG Xin ZHANG Hongran |
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| Affiliation: | College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China |
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| Abstract: | This paper presents a method of combining Deep Neural Network（DNN） regression model with i-vector/Probabilistic Linear Discriminant Analysis（PLDA） speaker recognition system. By fitting the nonlinear function relationship between the noisy and the clean speech i-vector using DNN, the approximate representation of the clean speech i-vector can be obtained to achieve the purpose of reducing the influence of the noise on the system performance. The feasibility and effectiveness of the proposed method are verified by the experiments on TIMIT data set. |
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| Keywords: | speaker recognition Deep Neural Network（DNN） i-vector |
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