# Target Speaker Extraction by Fusing Voiceprint Features
## 3 Experiments and Results
### Results
#### 3.2.1 Comparison of Model Performance
As a comparison, when adopting the ECC and CEC methods, PITNet and VoiceFilter both exhibit higher performances. The results indicate that the convolutional layer in the feature fusion process can effectively extract represented features from spectrograms or fused features and consequently improve the performances of the models. Compared with the MFCCs features, the d-vector offers less improvement to the speech separation models. When adopting the ECC fusing method, the SDR value of the DPCL model integrating the MFCCs is 10.660. By contrast, the SDR value of DPCL integrating the d-vector is 9.483, which is slightly smaller than its counterpart. Similarly, the SDR values of PITNet integrating the MFCCs and d-vector using the ECC method are 11.970 and 9.325, respectively. Furthermore, the SDR values of VoiceFilter integrating the MFCCs and d-vector using the ECC method are 11.470 and 8.159, respectively. The worse performances of the three speech separation models integrating the d-vector features may be caused by the nature of the d-vector. The d-vector features extracted highly depend on the training speech, while the MFCCs features only contain information about energy distributions over frequencies. Therefore, they are less influenced by the irrelevant characteristics of the training speech and more stable in the speech separation task. From Table 2, it can be seen that the qualities of the audios evaluated by the MOS are quite consistent with those evaluated by the SDR. The MOS values of the speech separation models without integrating voiceprint features are the smallest. After integrating the voiceprint features, the MOS values of these models are much improved, especially those models integrating the MFCCs features. The inconsistencies between Tables 2 and 3 are the performances of VoiceFilter integrating the MFCCs using the ECC and CEC feature fusion methods. The SDR results indicate that VoiceFilter integrating the MFCCs using the ECC method is better than that using the CEC method. However, the MOS results indicate that VoiceFilter integrating the MFCCs using the ECC method is a little worse than when using the CEC method. In fact, the performances of these two models are very similar. Considering that the MOS is a subjective evaluation measure related to the participant, there might be some inconsistencies between the two types of evaluation results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**None**} & \multicolumn{2}{c}{**MFCCs**} & \multicolumn{2}{c}{**d-Vector**} \\ \cline{3-6}  & & **DC** & **ECC** & **CEC** & **ECC** \\ \hline DPCL & 121.68 & **122.03** & **153.09** & **156.70** & **159.77** \\ PITNet & 530.89 & 531.96 & 612.29 & 619.75 & 632.22 \\ VoiceFilter & - & 346.99 & 402.84 & 407.85 & 414.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model sizes of the proposed methods (unit: MB). From Table 4, it can be seen that the size of DPCL is much smaller than that of PITNet and VoiceFilter. Nevertheless, it achieves comparable performances compared with the other two models. It even achieves the best performance when integrating the MFCCs features using the CEC fusion method. The lightweight characteristic of the DPCL model means it can be deployed in low-resource computing devices.