## 4 Experiments and Results
### Experimental Results  
Objective and subjective tests were conducted on the data of speakers from CMU ARCTIC and THCHS-30. The objective metric for evaluation was Mel-cepstral distortion (MCD) [15]:  
\[\text{MCD}\left[\text{dB}\right]=\frac{10}{ln\ 10}\sqrt{2\sum_{i=1}^{l} \left(m_{i}{}^{con}-m_{i}{}^{tar}\right)^{2}}, \tag{13}\]  
where \(m_{i}{}^{con}\) and \(m_{i}{}^{tar}\) are the MCEPs of the converted features and target features, respectively [22]. The results of the VC methods on CMU ARCTIC are given in Table 1, where smaller values indicate a lower distortion and a better performance on the conversion. The values of the source-target row are the MCD values of the source voice and the target voice without performing the conversion. According to the results in Table 1, the proposed method achieved a lower MCD than the competitive baselines in all the cases of conversion. This validated the efficacy of the proposed method. To provide a subjective evaluation of the proposed method, the mean opinion score (MOS) test on the naturalness and similarity of the converted voices was also conducted. Figure 4 shows the results of a subjective evaluation for different methods on the CMU ARCTIC dataset. In the MOS tests, listeners were asked to compare the four utterances of each set with the target voice and to rank the naturalness and similarity of the converted voices on a 5-point scale (5: excellent, 4: good, 3: fair, 2: poor, 1: bad). The subjective evaluation results are illustrated in Figure 4. The proposed approach significantly outperformed the baseline approaches in terms of speech naturalness and speaker similarity of the converted speech for both cross-gender and intra-gender conversions. By comparing Table 1 and Figure 4, it was found that BLSTM obtained a higher objective performance than GMM and DIFFGMM but failed to outperform them on subjective evaluations. In order to conduct an extensive comparison, experiments of cross-gender and intra-gender conversions were also performed on the Mandarin dataset THCHS-30. The results on the objective and subjective evaluations are given in Table 2 and Figure 5, respectively. A similar performance was observed as that from CMU ARCTIC. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**MCD (dB)**} \\ \hline
**Methods** & **M-M (bdl-jmk)** & **F-M (clb-jmk)** & **M-F (bdl-slt)** & **F-F (clb-slt)** \\ \hline Source–target & 10.682 & 12.763 & 10.755 & 9.896 \\ GMM & 9.084 & 10.732 & 8.836 & 9.635 \\ DIFFGMM & 8.513 & 10.214 & 9.253 & 9.716 \\ BLSTM & 8.210 & 9.401 & 7.485 & 8.032 \\ Proposed & **8.013** & **8.874** & **7.180** & **7.822** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean Mel-cepstral distortion (MCD) of all non-silent frames of the voice conversion (VC) methods on the CMU ARCTIC dataset (15 dB babble added to source). GMM: Gaussian mixture model, DIFFGMM: differential GMM. Figure 4: Results of the subjective evaluation for different methods on CMU ARCTIC (15 dB babble added to source): (**a**) mean opinion score (MOS) for similarity with 95% confidence intervals and (**b**) MOS for naturalness with 95% confidence intervals. In order to intuitively verify the noise robustness of different methods, the prediction accuracy of different methods for high-quefrency sub-band of the cepstrum was evaluated first. Furthermore, the high-quefrency sub-band Mel-cepstrum distortion (HQMCD) of the converted voice and the target voice was calculated. The conversion results of different methods for noisy speech are shown in Table 3. At the same time, in order to show the noise robustness of the proposed method in a straightforward way, the spectrogram of the converted voices obtained using the involved methods was extracted, as shown in Figure 6.