## 6 Results  
### Speaker Clustering  
Different lengths for RBM vectors, as well as for i-vectors, were evaluated using cosine scoring and the average linkage clustering algorithm. The results are shown in the second column of Table 1. From the Table, it can be observed that if the dimension is increased, the performance is improved, both in case of i-vectors and RBM vectors, in terms of Equal Impurity (EI). However, in the case of i-vectors, the best choice is 800 dimension. In case of RBM vectors, the 2000 dimensional RBM vectors perform better than the others. In this case, a relative improvement of 11% is achieved compared to 800 dimensional i-vectors. A further increase in the length of RBM vectors beyond 2000 degrades the performance in terms of EI. The third column of Table 1 compares the performance of the RBM vector with the baseline i-vectors in the case of the single linkage algorithm for clustering using cosine scoring. From the table it is seen that single linkage was a better choice for our experiments. In this case, a minimum EI of 37.14% is obtained with 2000 dimensional RBM vectors which has a relative improvement of 12% over 800 dimensional i-vectors. Finally, we evaluated the proposed system using PLDA scoring as well. The PLDA was trained using background RBM vectors for 15 iterations. The number of eigenvoices were set to 250, 450 and 500 for RBM vectors of dimensions 400, 800 and 2000, respectively. All the RBM vectors were subjected to length normalization prior to PLDA training. As per the previous results, we performed this experiment with the single linkage algorithm only. The results were compared with i-vectors in the fourth column of Table 1. It was observed that 800 and 2000 dimensional RBM vectors have a better EI compared to the respective similar dimensional i-vectors. In this case, the RBM vectors of dimension 2000 have a minimum EI of 31.68% which results in a relative improvement of 11% over the 800 dimensional i-vectors. However, in the case of 400 dimensions, the i-vectors outperform RBM vectors. The Impurity Trade-off (IT) curves for the baseline, as well as the proposed system, are shown in Figure 4. Figure 4a shows the evaluation of different dimensions of i-vectors and RBM vectors in the average linkage clustering using cosine scoring. It can be seen that RBM vectors of length 2000 gives a better performance than 800 dimensional i-vectors at all working points. On the other hand, RBM \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **EI\% (Cosine Average)** & **EI\% (Cosine Single)** & **EI\% (PLDA Single)** \\ \hline i-vector (400) & 49.19 & 46.26 & 36.16 \\ i-vector (800) & 46.66 & 42.19 & 35.91 \\ i-vector (2000) & 46.79 & 42.83 & 35.89 \\ RBM vector (400) & 51.36 & 39.66 & 37.36 \\ RBM vector (800) & 47.20 & 40.02 & 32.36 \\ RBM vector (2000) & 41.53 & 37.14 & 31.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of speaker clustering results for the proposed RBM vectors with i-vectors, in terms of Equal Impurity (EI) in %. The dimensions of vectors are given in parenthesis. Each column shows EI in % for different scoring and linkage combinations. vectors of dimensions 400, 800, 2400 and 3000 perform worse than i-vectors. It is observed that 400 and 800 dimensional RBM vectors could not capture enough information about the speaker while 2400 and 3000 dimensional RBM vectors include unnecessary information which degrades the performance. In Figure 3(b) we show a comparison of 2000 dimensional RBM vectors with 800 dimensional i-vectors using both cosine and PLDA scoring with the single linkage algorithm for clustering. The choices of dimensions are based on the previous experiments as 2000 dimensional RBM vectors and 800 dimensional i-vectors give the best results with cosine scoring and average linkage. From Figure 3(b), it can be seen that the RBM vectors perform better at all working points as compared to i-vectors using their respective cosine and PLDA scoring.