## 5 Results and Discussion
### Objective Evaluations  
We objectively evaluated the similarity between the target and converted speech by using Mel Cepstral Distortion (MCD), a widely used metric for spectral distortion in speech conversion, calculated using Equation (5). The MCD results of both our proposed and baseline methods were evaluated. \[\text{MCD [dB]}=\frac{10}{\ln 10}\sqrt{2\sum_{k=1}^{D}(C_{k}^{(c)}-C_{k}^{(t)})^{2}} \tag{5}\]  
where \(C^{(c)}{}_{k}\) and \(C_{k}^{(t)}\) represent the \(k^{th}\) coefficient of the converted and target MCCs vectors, respectively. D denotes the dimension of MCCs (39-dimensional features), calculated with a 25 ms window size and 10 ms window shift. To measure the intelligibility of the speech generated by the different systems we also calculated the Word Error Rates (WER) from the ASR transcriptions. We used the Word Error Rate Matlab toolbox [50] to calculate the Levenstein distance [51] between the hypothesis sentence (ASR transcription output) and the reference sentence (original utterance). The Levenstein distance WER considers insertions, deletions, and substitutions observed in the ASR transcription output. The WER formula is given in the following Equation (6). The smaller the WER scores, the better the speech intelligibility. \[\text{WER}=\frac{\text{Substitutions + Insertions + Deletions}}{\text{Total number of words in reference sentence}}\times 100 \tag{6}\]  
The MCDs results obtained with the proposed and baseline methods on the validation set are summarized in Table 1. A lower MCD value signifies the better performance of the VC system. Comparing the proposed method to the baseline methods S3 et S4, which are based on non-parallel training data, the results clearly show that the proposed method performs better for all conversion pairs. The average MCD values obtained by the method we propose are significantly lower than those obtained by S4, namely 6.46 dB vs. 7.19 dB, but almost equivalent to those obtained by S3, i.e., 6.46 vs. 6.48. We can explain this by using the autoregressive structure that incorporates the outputs from the previous step to predict the outputs of the next step, which results in smoother waveforms. We can confirm this by using the efficient LPCNet vocoder to generate waveforms, while S3 and S4 adopt the WORLD [52] and WaveNet vocoders, respectively. On the other hand, by comparing the proposed method to the baseline methods based on parallel training data, we can show that our system produces considerably better MCD values than those obtained by S2 for all conversion pairs. For example, in the case of the RMS-to-SLT conversion pair, our proposal achieved an MCD value of 6.37, whereas S2 obtained a value of 7.34, resulting in a relative reduction of 0.97. Furthermore, we compare the baseline S1 and our method based on parallel and non-parallel voice conversion. We observe that the proposed method gives slightly better MCD values than S1, i.e., an average of 6.86 vs.6.46. Table 2 gives the WER scores obtained for our VC method and the state-of-the-art methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**MCD (dB)**} \\ \cline{2-6}  & & **BDL\(\rightarrow\)SLT** & **CLB\(\rightarrow\)SLT** & **RMS\(\rightarrow\)SLT** & **Average** \\ \hline \multirow{2}{*}{Para} & S1 [11] & 7.08 & 6.63 & 6.88 & 6.86 \\ \cline{2-6}  & S2 [10] & 7.22 & 6.64 & 7.34 & 7.06 \\ \hline \multirow{4}{*}{N-Para} & S3 [49] & 6.57 & 6.47 & 6.40 & 6.48 \\ \cline{2-6}  & S4 [41] & 7.17 & 7.31 & 7.11 & 7.19 \\ \cline{1-1} \cline{2-6}  & Proposed & 6.53 & 6.49 & 6.37 & 6.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mel Cepstral Distortion (dB) results of baseline and proposed methodsThe proposed method demonstrated lower WER scores (i.e., better intelligibility) compared to state-of-the-art methods for both male and female speakers. From the female speaker's WER, it can be seen that our method notably reduces errors compared to the male speaker's WER.