# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 5 Results and Discussion  
According to different evaluation metrics for both separation and identification as illustrated in Section 3.3, the proposed system successfully separated and identified the main speaker using deep learning. The results showed SNR training curves for various numbers of speakers N = 2, 3, 4, as shown in Figure 6. The used model trained faster for a smaller number of speakers, as reported in Table 1. It demonstrated the performance of the used separation model as a function of the number of mixed speakers compared to the WSJ0-2 mix, WSJ0-3 mix and WSJ0-3 mix dataset. In [25], all reported numbers are the scale invariant signal-to-noise-ratio improvement (SI-SNRi) over the input mixture. It is noted that the SI-SNRi of our self-made experiment for two speakers is less than that of WSJ0-2 mix data. One possible explanation for this can be considered, as the relative speakers had more common features which made it harder to be separated than random speakers in the WSJ0-2 mix dataset. While the other SI-SNRi, in case of three or four speakers, gave us better results. Figure 7 depicts the SI-SNRi curves for various mixed numbers of speakers. The SI-SNRi increased in parallel with the increasing number of epochs in training, which indicated a better performance of the used model.  
Figure 6: Training curves of our model for various numbers of speakers N = 2, 3, 4.  
On our self-made datasets, speaker identification was carried out successfully and with excellent results, where the accuracy was 96%. Thus, the MFCC-GMM model achieves satisfying performance. Hence, the separated and identified target speaker is ready to be sent alone over a VoIP system without any noise or other speakers attached to it.  
The processing time for each block is calculated as it is considered a critical factor for real-time applications. Table 2 shows the process time (testing time) for separation and identification processes separately, then the overall time needed before sending the target speaker's voice over the call.  
It is noted that, while increasing the number of mixed speakers, the time needed for separation is increased and so is the overall time. These results of processing time are depicted using only one GPU. Evidently, the processing time can be reduced significantly when using multi-GPU support systems which are suitable for speeding up signal-processing and real-time applications, as explained in [32; 33]. The geometric mean of all speedups tested across all datasets for that configuration is the acceleration of a given primitive using a specified number of GPUs. The majority of primitives scale well from one to six GPUs, with various datasets for breadth-first search (BFS), single-source shortest path (SSSP), connected components (CC), betweenness centrality (BC), and PageRank (PR) being 2.63, 2.57, 2.00, 1.96, and 3.86 times faster using six (K40) GPUs as illustrated in detail in [32].  
\begin{table}
\begin{tabular}{c c c c} \hline  & **2 spk** & **3 spk** & **4 spk** \\ \hline Self-Made & 18.4168 & 17.5052 & 15.6260 \\ WSJ0-mix & 20.12 & 16.85 & 12.88 \\ \hline \end{tabular}
\end{table}
Table 1: SI-SNRi for different numbers of mixed speakers.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Separation** & **Identification** & **Total Processing** & **Estimated Processing** \\  & **Time** & **Time** & **Time** & **Time after GPU** \\ \hline
2 Spk & 6.60 s & 1.03 s & 6.63 s & 1.71 s \\ \hline
3 Spk & 6.53 s & 1.04 s & 7.57 s & 1.96 s \\ \hline
4 Spk & 6.782 s & 1.06 s & 7.743 s & 2.00 s \\ \hline \end{tabular}
\end{table}
Table 2: Process time for different numbers of mixed speakers.  
Figure 7: SI-SNRi curves for various mixed numbers of speakers.