## 3 Results
### Objective Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Speech Types**} & \multirow{2}{*}{**U/V Type**} & \multicolumn{2}{c}{**Traditional Features**} & \multicolumn{2}{c}{**Mel-Spectrogram**} \\ \cline{3-6}  & & **U\({}_{est}\)** & **V\({}_{est}\)** & **U\({}_{est}\)** & **V\({}_{est}\)** \\ \hline \multirow{2}{*}{CLDNN-FT} & \(U_{tar}\) & 83.42\% & 16.58\% & 84.35\% & 15.65\% \\  & \(V_{tar}\) & 2.45\% & 97.55\% & 2.88\% & 97.12\% \\ \hline \multirow{2}{*}{VC3-FT} & \(U_{tar}\) & 90.21\% & 9.79\% & 92.13\% & 7.87\% \\  & \(V_{tar}\) & 2.32\% & 97.68\% & 2.05\% & 97.95\% \\ \hline \multirow{2}{*}{2C1T2C-FT} & \(U_{tar}\) & 90.58\% & 9.42\% & 93.1\% & 6.90\% \\  & \(V_{tar}\) & 1.68\% & 98.32\% & 1.75\% & 98.25\% \\ \hline \multirow{2}{*}{CLDNN-VT} & \(U_{tar}\) & 85.44\% & 14.56\% & 84.89\% & 15.11\% \\  & \(V_{tar}\) & 3.89\% & 96.11\% & 3.78\% & 96.22\% \\ \hline \multirow{2}{*}{VC3-VT} & \(U_{tar}\) & 92.43\% & 7.57\% & 93.86\% & 6.14\% \\  & \(V_{tar}\) & 2.22\% & 97.78\% & 1.33\% & 98.67\% \\ \hline \multirow{2}{*}{2C1T2C-VT} & \(U_{tar}\) & 92.15\% & 7.85\% & 95.12\% & 4.88\% \\  & \(V_{tar}\) & 1.17\% & 98.83\% & 1.18\% & 98.82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: U/V analysis for continuous Mandarin EL speech under different approaches. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Speech Types**} & \multicolumn{2}{c}{**Traditional Features**} & \multicolumn{2}{c}{**Mel-Spectrogram**} \\ \cline{2-5}  & **log F0 RMSE** & **F0 CC** & **log F0 RMSE** & **F0 CC** \\ \hline CLDNN-FT & 0.1321 & 0.6783 & 0.1300 & 0.7247 \\ VC3-FT & 0.1214 & 0.7223 & 0.1205 & 0.7218 \\
2C1T2C-FT & 0.1131 & 0.6287 & 0.1121 & 0.6025 \\ CLDNN-VT & 0.1633 & 0.7472 & 0.1131 & 0.7794 \\ VC3-VT & 0.1037 & 0.7122 & 0.1027 & 0.7081 \\
2C1T2C-VT & 0.1011 & 0.6275 & 0.1005 & 0.5934 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Objective evaluations of converted F0 pattern. Figure 4a shows the acoustic features analysis of Mandarin EL speech with fixed tone (EL-FT); Figure 4b shows the acoustic features analysis of Mandarin EL speech with variable tone (EL-VT); Figure 4c shows the acoustic features analysis of enhanced Mandarin EL speech with fixed tone using CLDNN (CLDNN-FT); Figure 4d shows the acoustic features analysis of CLDNN-VT; Figure 4e shows the acoustic features analysis of enhanced Mandarin EL speech with fixed tone using VC3 (VC3-FT); Figure 4f shows the acoustic  
Figure 5: MOS for intelligibility with 95% confidence intervals. Figure 6: MOS for naturalness with 95% confidence intervals. features analysis of VC3-VT; Figure 4g shows the acoustic features analysis of enhanced Mandarin EL speech with fixed tone by our proposed approach (2C1T2C-FT); Figure 4h shows the acoustic features analysis of 2C1T2C-VT; and Figure 4i shows the acoustic features analysis of normal speech. In Figure 4, the top part of every subfigure shows waveform signals; the middle part shows pitch analysis; and the bottom part shows the narrow-band spectrogram analysis. For waveform analysis, based on the EL-FT and EL-VT shown in Figure 4a,b, the radiation noise is all effectively cancelled in enhanced speech. For pitch analysis, the pitch lines of 2C1T2C-FT and 2C1T2C-VT are closest to that of the normal speech. For example, from the comparison of the Figure 4c,e,g, the utterance of VC3-FT has three correct tones (\"ge4\", \"zhu2\" and \"yi4\") and four tones of 2C1T2C-FT are correct (\"yi2\", \"ge4\", \"zhu2\" and \"yi4\"). However, the utterance of CLDNN-FT has only one correct tone (\"yi4\"). In addition, based on the Figure 4d,f,h, there are six correct tones in all of the three utterances. This result illustrates that all approaches can modify the tones of EL speech. However, the CLDNN-based approach has a worse performance in enhancing continuous Mandarin EL speech with fixed tone. In addition, narrow-band spectrogram analysis is applied to show the enhancement of temporal details.