# U-Vectors: Generating Clusterable Speaker Embedding from Unlabeled Data
## 4 Experiments and Evaluations
### Result Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Impurity = 0**} & \multicolumn{4}{c}{**Impurity = 0.05**} & \multicolumn{4}{c}{**Impurity = 0.1**} \\ \cline{2-10}  & **ACC** & **NMI** & **ARI** & **ACC** & **NMI** & **ARI** & **ACC** & **NMI** & **ARI** \\ \hline \multicolumn{10}{c}{Speakers = 25} \\ Train & 0.936 & 0.989 & 0.911 & 0.954 & 0.992 & 0.940 & 0.956 & 0.993 & 0.943 \\ Ground & 0.946 & 0.983 & 0.935 & 0.866 & 0.948 & 0.808 & 0.806 & 0.918 & 0.673 \\ \hline \multicolumn{10}{c}{Speakers = 50} \\ Train & 0.730 & 0.947 & 0.555 & 0.741 & 0.950 & 0.576 & 0.755 & 0.953 & 0.594 \\ Ground & 0.951 & 0.989 & 0.933 & 0.906 & 0.971 & 0.857 & 0.863 & 0.957 & 0.778 \\ \hline \multicolumn{10}{c}{Speakers = 100} \\ Train & 0.482 & 0.891 & 0.133 & 0.511 & 0.900 & 0.186 & 0.588 & 0.920 & 0.309 \\ Ground & 0.924 & 0.984 & 0.921 & 0.885 & 0.970 & 0.831 & 0.840 & 0.949 & 0.700 \\ \hline \multicolumn{10}{c}{Speakers = 146} \\ Train & 0.490 & 0.780 & 0.109 & 0.493 & 0.900 & 0.150 & 0.515 & 0.906 & 0.190 \\ Ground & 0.932 & 0.987 & 0.916 & 0.797 & 0.949 & 0.678 & 0.713 & 0.923 & 0.607 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The table benchmarks the pairwise architecture in LIBRI dataset with four groups of speakers, 25, 50, 100, and 150. For each group of speakers, the table also considers three segmentation impurities, 0, 0.05, and 0.1 to illustrate the shortcomings of incorrect segmentation, for fully unsupervised speaker recognition strategy. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Impurity = 0**} & \multicolumn{4}{c}{**Impurity = 0.05**} & \multicolumn{4}{c}{**Impurity = 0.1**} \\ \cline{2-10}  & **ACC** & **NMI** & **ARI** & **ACC** & **NMI** & **ARI** & **ACC** & **NMI** & **ARI** \\ \hline \multicolumn{10}{c}{Speakers = 25} \\ Train & 0.772 & 0.950 & 0.627 & 0.868 & 0.974 & 0.795 & 0.893 & 0.980 & 0.842 \\ Ground & 0.934 & 0.979 & 0.919 & 0.886 & 0.958 & 0.822 & 0.777 & 0.924 & 0.630 \\ \hline \multicolumn{10}{c}{Speakers = 50} \\ Train & 0.663 & 0.930 & 0.436 & 0.659 & 0.930 & 0.435 & 0.678 & 0.935 & 0.464 \\ Ground & 0.924 & 0.979 & 0.898 & 0.849 & 0.948 & 0.755 & 0.764 & 0.915 & 0.623 \\ \hline \multicolumn{10}{c}{Speakers = 100} \\ Train & 0.509 & 0.899 & 0.175 & 0.523 & 0.904 & 0.208 & 0.590 & 0.920 & 0.305 \\ Ground & 0.889 & 0.973 & 0.847 & 0.809 & 0.948 & 0.723 & 0.719 & 0.914 & 0.544 \\ \hline \multicolumn{10}{c}{Speakers = 150} \\ Train & 0.471 & 0.861 & 0.117 & 0.500 & 0.902 & 0.155 & 0.519 & 0.907 & 0.194 \\ Ground & 0.815 & 0.955 & 0.734 & 0.718 & 0.924 & 0.645 & 0.620 & 0.896 & 0.480 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The table benchmarks the pairwise architecture in TIMIT dataset with four groups of speakers, 25, 50, 100, and 150. For each group of speakers, the table also considers three segmentation impurities, 0, 0.05, and 0.1 to illustrate the shortcomings of incorrect segmentation, for fully unsupervised speaker recognition strategy. of speakers to 50 balances the speech variations in the training data and causes an increase in accuracy.