# Target Speaker Extraction by Fusing Voiceprint Features
## 3 Experiments and Results
### Results
#### 3.2.2 Comparison of Estimated Spectrogram  
A noisy audio together with the target clean speech were randomly selected to analyze the characteristics of the magnitude spectrograms estimated by the proposed Tes-FV model. Figure 2 shows the log-magnitude spectrogram of the selected noisy audio and the target clean speech. Figure 3 shows the estimated spectrogram and the estimation error of the DPCL model with and without voiceprint feature integration, when evaluated on the selected noisy audio. In the subfigures of the _estimation error_, the color indicates the absolute distance between the estimated spectrogram and the spectrogram of the target clean speech. The darker the point, the greater the estimation error. Compared with Figure 2(a)(ii), Figure 2(b)-e(ii) is much lighter in color, which indicates that the integration of the MFCCs/d-vector features improves the accuracy of estimation, especially in the 400-500 frame segment. Figure 4 shows the estimated spectrogram and the estimation error of the PITNet model with and without voiceprint feature integration. Similar to Figure 3, the estimation error of the PITNet model integrating voiceprint features is much smaller than that of the original PITNet model. However, Figure 2(b)-e(ii) are darker than Figure 2(b)-e(ii), which means that the estimation error of PITNet is larger than that of DPCL. Moreover, DPCL assigns each T-F bin to only one speaker. As a comparison, PITNet predicts the percentage of the different speaker's speech in each T-F bin. Therefore, the magnitude spectrograms estimated by PITNet look smoother than those of DPCL. Figure 5 shows the estimated spectrogram and the estimation error of the VoiceFilter model integrating voiceprint features. From Figure 4(a)-d(ii), it can be seen that the estimation error of the model integrating the MFCCs features distributes more evenly compared with that of the model integrating the d-vector features. However, in certain T-F bins, the estimation error of the model integrating the d-vector features is smaller than that of the model integrating the MFCCs features. For example, in Figure 4(d)(ii), the area located in [150, 250; 25 Hz, 40 Hz] is brighter than the corresponding area in Figure 4(a)-c(ii). A conclusion can be drawn that VoiceFilter integrating the d-vector features shows a better performance in certain time frame and frequency ranges, while VoiceFilter integrating the MFCCs features shows an overall better performance. Figure 2: The magnitude spectrogram of the noisy audio and the target clean speech. The \(x\)-axis represents the time frame, the \(y\)-axis represents the frequency, and the unit of color scale is dB. (**a**) The magnitude spectrogram of the noisy audio; (**b**) the magnitude spectrogram of the target clean speech. Figure 3: The estimated spectrograms and estimation error diagrams of DPCL model. The \(x\)-axis represents the time frame, the \(y\)-axis represents the frequency, and the unit of color scale is dB. (**a**) The estimated magnitude spectrogram (i) and the estimation error (ii) of the DPCL model without voiceprint feature integration. (**b**) The estimated magnitude spectrogram (i) and the estimation error (ii) of the DPCL model integrating MFCCs features using DC fusion method. (**c**) The estimated magnitude spectrogram (i) and the estimation error (ii) of the DPCL model integrating MFCCs features using ECC fusion method. (**d**) The estimated magnitude spectrogram (i) and the estimation error (ii) of the DPCL model integrating MFCCs features using CEC fusion method. (**e**) The estimated magnitude spectrogram (i) and the estimation error (ii) of the DPCL model integrating d-vector features using ECC fusion method. Figure 4: The estimated spectrograms and estimation error diagrams of PITNet model. The \(x\)-axis represents the time frame, the \(y\)-axis represents the frequency, and the unit of color scale is dB. (**a**) The estimated magnitude spectrogram (i) and the estimation error (ii) of the PITNet model without voiceprint feature integration.