# Target Speaker Extraction by Fusing Voiceprint Features
## 3 Experiments and Results
### Results  
#### 3.2.1 Comparison of Model Performance  
The SDR results and the MOS results of Tse-FV model using three feature fusion methods (i.e., DC, ECC, and CEC) are shown in Tables 2 and 3, respectively. The model sizes of Tse-FV based on DPCL, PITNet, and VoiceFilter are shown in Table 4. In the second column of Tables 2-4, \"none\" represents the settings of not integrating with voiceprint features. Therefore, the \"none\" columns in Tables 2 and 3 give the original performance of the DPCL and PITNet models in the two-speaker speech separation task. Because VoiceFilter, as a speaker-dependent model, is designed to adopt the d-vector to extract the target speaker's speech, the \"none\" setting is inapplicable for VoiceFilter. The third column \"MFCC\" gives the performance of three models integrating the MFCCs features. The last column \"d-vector\" gives the performance of three models integrating the d-vector features. The d-vector was fused with the magnitude spectrogram only when using the ECC method. It is because d-vector is a compact representation of the target speaker and irrelevant to time and frequency. Conversely, the expanded convolutional layer was used to extract high-level features reflecting the relationship between time and frequency. Therefore, only the ECC method was adopted when integrating the d-vector features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**None**} & \multicolumn{3}{c}{**MFCCs**} & \multicolumn{2}{c}{**d-Vector**} \\ \cline{3-6}  & & **DC** & **ECC** & **CEC** & **ECC** \\ \hline DPCL & 1.57 & 3.37 & 3.67 & **4.23** & 1.63 \\ PITNet & 1.20 & 3.67 & **4.04** & 3.87 & 1.77 \\ VoiceFilter & - & 3.17 & 3.83 & **4.01** & 1.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average MOS values corresponding to the three speech separation models with and without voiceprint feature integration. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**None**} & \multicolumn{3}{c}{**MFCCs**} & \multicolumn{2}{c}{**d-Vector**} \\ \cline{3-6}  & & **DC** & **ECC** & **CEC** & **ECC** \\ \hline DPCL & 8.279 & 10.530 & 10.660 & **12.430** & 9.483 \\ PITNet & 3.371 & 10.540 & **11.970** & 11.940 & 9.325 \\ VoiceFilter & - & 10.420 & **11.470** & 11.160 & 8.159 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average SDR values corresponding to the three speech separation models with and without voiceprint feature integration (unit: dB). From Table 3, it can be seen that the original performances of the speaker-independent models, i.e., DPCL and PITNet, are the worst when evaluated on the dataset with their SDR values of 8.279 and 3.371. After integrating the MFCCs and d-vector features, the performances of DPCL and PITNet are both greatly improved. When integrating the MFCCs features, the best performance of DPCL is improved from 8.279 to 12.430 using CEC fusing method. Furthermore, the best performance of PITNet is improved from 3.371 to 11.970 using the ECC fusing method. The performances of the two models are improved by 50% and 255%, respectively. When integrating the d-vector features, the performances of DPCL and PITNet are also improved to 9.483 and 9.325. These results indicate that the voiceprint features, which reflect the characteristics of the speaker's voice, provide useful information for the target speech extraction and have great potential in improving the performances of the speaker-independent models. When integrating the MFCCs features using the DC, ECC, and CEC fusing methods, the best performance of the DPCL model is achieved by adopting the CEC method with SDR value 12.430. The DC fusing method exhibits the worst performance compared with that of the ECC and CEC methods with an SDR value of only 10.530. Such pattern is also found from the results of PITNet and VoiceFilter. When adopting the DC method, PITNet and VoiceFilter exhibit the worst performance with SDR values of 10.540 and 10.420.