将GMM-UBM分数转换为等同的准确度百分比 [英] Convert GMM-UBM scores to equicalent accuracy percent

问题描述

我已经建立了一个GMM-UBM模型用于说话人识别.模型的输出适用于每个说话者，通过对数似然比来计算一些分数.现在，我想将这些可能性得分转换为0到100之间的等值数字.有人可以指导我吗?

I have constructed a GMM-UBM model for the speaker recognition purpose. The output of models adapted for each speaker some scores calculated by log likelihood ratio. Now I want to convert these likelihood scores to equivalent number between 0 and 100. Can anybody guide me please?

推荐答案

没有简单的公式.您可以做

There is no straightforward formula. You can do simple things like

prob = exp(logratio_score)

，但是这些可能无法反映您的数据的真实分布.计算得出的样本概率百分比将不会均匀分布.

but those might not reflect the true distribution of your data. The computed probability percentage of your samples will not be uniformly distributed.

理想情况下，您需要获取一个大数据集并收集有关您对于什么分数所具有的接受/拒绝率的统计信息.然后，一旦构建了直方图，您就可以通过该频谱图将分数差异归一化，以确保如果您看到一定的分数差异，则可以接受30％的受试者.该归一化将允许您创建均匀分布的概率百分比.参见例如如何在存在零的像元的情况下，从2x2表计算似然比的置信区间

Ideally you need to take a large dataset and collect statistics on what acceptance/rejection rate do you have for what score. Then once you build a histogram you can normalize the score difference by that spectrogram to make sure that 30% of your subjects are accepted if you see the certain score difference. That normalization will allow you to create uniformly distributed probability percentages. See for example How to calculate the confidence intervals for likelihood ratios from a 2x2 table in the presence of cells with zeroes

在说话者识别系统中很少解决此问题，因为置信区间不是您真正想要显示的.您需要一个简单的接受/拒绝决定，为此您需要知道错误拒绝的数量和接受率.因此，仅找到一个阈值而不建立整个分布就足够了.

This problem is rarely solved in speaker identification systems because confidence intervals is not what you want actually want to display. You need a simple accept/reject decision and for that you need to know the amount of false rejects and accept rate. So it is enough to find just a threshold, not build the whole distribution.

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