# An Electroglottograph Auxiliary Neural Network for Target Speaker Extraction
## 4 Experiments and Results
### Results  
#### 4.2.1 Comparison to SpeakerBeam  
To evaluate the effectiveness of using EGG signals to solve speech extraction tasks, we compared the performances of FD-SpeakerBeam and SpeakerBeam based on EGG signals. For the SpeakerBeam experiments, we provided the network with extra information about the target speaker, including the extra sentences from the datasets. For the EGG_Aux network experiments, we input EGG signals into the network. In addition, we utilized the time-domain characteristics of the EGG signals to process the silent segments from the mixed speech before the Pre_EGG_Aux experiments. Therefore, both frequency domain and time domain features were utilized in this work. An example of the estimated waveforms is shown in Figure 9.  
For a fair comparison between the different speech extraction methods, the same amount of extra information was used from both datasets and we used the SDRi and SISDRi metric to compare the results. Table 3 shows the SDRi and SISDRi values from the different experiments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Training Sets**} & \multicolumn{3}{c}{**Test Sets**} \\  & **Speakers** & **Mixtures** & **Target** & **Speakers** & **Mixtures** & **Target** \\ \hline CDESD & 20 & 63,000 & 9000 & 20 & 7000 & 1000 \\ EMO-DB & 10 & 16,200 & 3240 & 10 & 1800 & 360 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The total number of speakers and the numbers of target speaker and mixed speech samples. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Training Dataset** & **SDRi (dB)** & **SISDRi (dB)** \\ \hline SpeakerBeam & CDESD & 3.43 & 4.39 \\ EGG\_Aux & CDESD & 4.55 & 5.25 \\ Pre\_EGG\_Aux & CDESD & 4.58 & 5.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparison between the SDRi and SISDRi on the CDESD for SpeakerBeam and the two methods proposed in our paper. The parameters are the average of all test results. The results showed that in the field of deep learning, EGG signals could provide more information and a better target speech extraction performance than common speech signals, EGG signals could also be obtained in noisy environments, which offers extensive prospects. The first set of experiments compared the performances of the considered methods on the CDESD and EMO-DB datasets, which were originally created for emotion recognition. The first part of Table 3 shows the results for the SpeakerBeam network, which achieved an SISDRi of 4.39 dB for mixtures of two voices on CDESD. The second part of the table shows that the EGG signals achieved a better performance than normal speech signals for the SpeakerBeam network. The SDRi was 4.58 dB and the SISDRi was 5.28 dB, which were 33% and 20% higher in the validation sets, respectively, showing that the extracted frequency-domain features of the EGG signals provided more information for the network to solve the speech extraction problem. The experiment also utilized the time-domain features of the EGG signals, because previous experiments have only focused on the frequency domain and have overlooked the time-domain features of EGG signals, which are consistent with normal speech signals in silent and unvoiced segments. The results showed that the time-domain features of the EGG signals improved the performance of the networks at a low level. The reason for the this could be that time-domain features were taken into consideration in the STFTs. Moreover, the algorithm that filtered out the silent segments during prepocessing could have filtered out some of the unvoiced or voiced segments by mistake. Despite the slight improvement in the SDRi and SISDRi metrics, the results showed that the pre_processing of EGG signals could help to solve the target speech extraction problem. To verify this improvement in solving speech extraction task using EGG signals, we carried out further experiments on EMO-DB dataset, which was recorded in German and has 2 s speech samples compared to the 1 s speech samples in the CDESD.