说话人识别中线性和非线性校正方法的比较
A comparison of linear and non-linear calibrations for speaker recognition
摘要：
在最近的工作生成和判别评分对数似然比校准，它表明，线性变换给出良好的准确性，只有在有限的范围内的操作点。此外，这些方法需要定制校准训练目标函数，以便瞄准最佳精度的期望区域。在这里，我们将线性配方推广到非线性配方。我们实验了一个非线性，非参数，歧视PAV解决方案，以及参数，生成，最大似然解决方案，使用高斯，学生T和正常逆高斯分数分布。对NIST SRE'12分数的实验表明，非线性方法提供了更宽范围的最佳准确度，并且可以在不必求助于目标函数剪裁的情况下进行训练。
英文摘要：
In recent work on both generative and discriminative score to log-likelihood-ratio calibration, it was shown that linear transforms give good accuracy only for a limited range of operating points. Moreover, these methods required tailoring of the calibration training objective functions in order to target the desired region of best accuracy. Here, we generalize the linear recipes to non-linear ones. We experiment with a non-linear, non-parametric, discriminative PAV solution, as well as parametric, generative, maximum-likelihood solutions that use Gaussian, Student's T and normal-inverse-Gaussian score distributions. Experiments on NIST SRE'12 scores suggest that the non-linear methods provide wider ranges of optimal accuracy and can be trained without having to resort to objective function tailoring.
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