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首页> 外文期刊>Audio, Speech, and Language Processing, IEEE Transactions on >Robust Session Variability Compensation for SVM Speaker Verification
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Robust Session Variability Compensation for SVM Speaker Verification

机译：用于SVM说话人验证的强大会话可变性补偿

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摘要

This paper presents an enhanced nuisance attribute projection (NAP) method to improve the performance of speaker verification systems in mismatched train and test conditions. Unlike the conventional NAP training method that does not take any scheme to discriminate the source of nuisance, the proposed method quantitatively estimates the source of nuisance based on the statistics of given background speakers' eigenvalues. The estimated values are used for defining a discriminative weight for each of background speakers and selectively including the statistics of between-class scatter or of within-class scatter from them. Through the scheme, we intend to design a more robust projection matrix which involves less speaker-dependent or speaker-intrinsic variability while including more latent nuisance factors beyond the common within-class scatter of backgrounds. Experimental results on the recent NIST SRE evaluations demonstrate that the proposed algorithms produce consistent improvement over the previous NAP approaches.
机译：本文提出了一种增强的干扰属性投影（NAP）方法，以提高在不匹配的火车和测试条件下说话人验证系统的性能。与传统的NAP训练方法不采用任何方法来区分滋扰源的方法不同，该方法基于给定背景说话者特征值的统计信息来定量估计滋扰源。估计值用于定义每个背景说话者的判别权重，并从中选择性地包括类间散布或类内散布的统计信息。通过该方案，我们打算设计一个更健壮的投影矩阵，该矩阵涉及较少的说话者相关性或说话者本征可变性，同时包括除常见的类内背景散布以外的更多潜在干扰因素。最近的NIST SRE评估的实验结果表明，所提出的算法比以前的NAP方法产生了持续的改进。

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