## 6 Results
### Speaker Tracking

Table 2: Comparison of speaker tracking results for the proposed RBM vectors with 800 dimensional i-vectors, in terms of EER in %. The lengths of RBM vectors and i-vectors are given in parenthesis. Column 2 and 3 represents EER in % for cosine and PLDA Scoring respectively. Figure 5: Speaker segmentation results in terms of False Alarm Rate (FAR) and Miss Detection Rate (MDR) in %. Results are obtained using different Window sizes (\(d\)) and a constant shift i.e., \(\Delta=0.25\) s. A collar of \(\pm 0.25\) s is accepted around a speaker change point. 500 for RBM vectors of lengths 600, 800 and 2000 respectively. All the RBM vectors are subjected to length normalization prior to PLDA training. From the table, it is clear that the 2000 dimensional RBM vector/PLDA system outperforms the 800 dimensional i-vector/PLDA system by a relative improvement of 7.74%. In the case of PLDA post processing, increasing the dimensions of the RBM vectors will increase the computational costs of PLDA training. This is because the PLDA model is trained on higher dimensional background RBM vectors. Figure 6 shows the comparison of Detection Error Trade-off (DET) curves for the baseline as well as the proposed system. These graphs are obtained by tuning the \(\lambda\) parameter in Equation (8). In Figure 5(a) we have evaluated different lengths of RBM vectors by comparing with i-vectors using cosine scoring. It can be observed that RBM vector of lengths 800 and 2000 give a better performance than the baseline i-vectors at low MST points only. An RBM vector of length 600 can be comparable with baseline i-vectors in this region. On the other hand, at low FA points the baseline i-vectors outperform RBM vectors of either length. However, at very few working points in low FA region, the RBM vector of length 2400 can be comparable with the baseline i-vectors. In Figure 5(b) we have shown a comparison of 2000 dimensional RBM vector (which gives the best results with the cosine scoring method) with baseline i-vectors using both cosine and PLDA scoring. From the figure, a similar kind of behavior is observed for RBM vectors using PLDA scoring as well. It can be seen that the 2000 dimensional RBM vector outperforms the baseline i-vectors in low MST regions using both cosine and PLDA scoring. However, in the low FA regions, the i-vector/PLDA framework still performs better which was also the case using the cosine scoring method. The plots in Figure 6 are not very smooth and seem insensitive to \(\lambda\). This is because the segments are not necessarily of the same duration. As the error (FA and MST) depends on the duration of segments, a false acceptance/rejection does not affect the error in a linear manner. Sometimes a certain value of lambda will falsely accept/reject a long segment which will highly affect the error. While in the case of a short segment a false acceptance/rejection will have a minimum reflection in the error. We show the error variations of our experiments in Figure 7. The box plots in Figure 7 show the EER distribution of 38 test shows for the proposed RBM vector and i-vector based speaker tracking systems. Each box plot shows the minimum, lower quartile, mean, upper quartile, and maximum EER scores. Figure 6: Comparison of Detection Error Trade-off (DET) curves for the proposed RBM vectors with 800 dimensional i-vectors. Different lengths of RBM vectors are evaluated using cosine and PLDA scoring. The lengths of RBM vectors and i-vectors are given in parenthesis. Figure 6(a),b depict the box plots for different lengths of RBM vectors and 800 dimensional i-vectors using cosine and PLDA scoring, respectively. Figure 6(a) shows that RBM vectors reduce the EER variations as compared to i-vectors. It is seen that when we increased the length of RBM vectors, the EER variation was reduced further. The lowest EER variation is observed for 2000 dimensional RBM vector. Similarly, Figure 6(b) shows the same behaviour in EER variation using PLDA scoring.