# Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-Based Time Warping
## 5 Results and Discussion
### Subjective Evaluation  
We also conducted an ABX test to subjectively evaluate the quality of the resynthesized speech signals. To implement the test, we used the web-based BeaqleJS framework [40]. Twenty-seven listeners (recruited among students enrolled in our courses) participated in the test, who had to judge which of two versions of the same signal produced by any combination of two of the five systems in Table 2 was more similar to a reference (one of the signals recorded by the subjects). Each listener evaluated 10 sample pairs for each of the 10 possible system combinations (i.e., 100 pairs evaluated in total by each listener). For this task, only the \"basic\" systems in Table 2 were evaluated (i.e., without the use of the autoencoder-loss nor the private variables), because this setting produced the best overall objective results. The test was conducted in a quiet room while listeners wore good-quality headphones. Listeners were instructed to set the volume at at comfy level at the start of the test. The total duration of the test was approximately 20 min.  
Figure 3 shows the results of the listening test. The most preferred system by a large margin was our algorithm TRANSIENCE using the CL function, being this system on par with the Oracle system. Similar to the triplet loss used in applications such as face or voice biometrics [41; 42], we speculate that the CL loss function allows for a more precise way to measure the similarity between pairs of latent vectors sampled from different views and, hence, to more accurately training the DNNs used by our algorithm.  
Interestingly, the CTW system obtained higher preference scores than its nonlinear version (TRANSIENCE+CCA) and that of the MMI system. It may be because the optimization process became stuck in poor local-minima for the latter systems. However, more research is needed to shed some light into this problem.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **MGCC** & **BAP** & **F0** & **Voicing** \\
**Method** & **MCD (dB)** & **RMSE (dB)** & **RMSE (Hz)** & **Err. Rate (\%)** \\ \hline Oracle & 7.81 & 0.43 & 14.75 & 23.79 \\ \hline CTW & 8.55 & 0.59 & 15.98 & 23.08 \\ +autoenc. & 9.20 & 0.88 & 16.70 & 25.30 \\ +priv. vars. & 8.83 & 0.58 & 15.74 & **21.47** \\ \hline TRANSIENCE-CCA & 9.37 & 0.85 & 16.40 & 27.95 \\ +autoenc. & 10.02 & 1.46 & 15.95 & 34.08 \\ +priv. vars. & 10.48 & 1.24 & 15.79 & 31.88 \\ \hline TRANSIENCE-MMI & 9.74 & 0.69 & 16.43 & 22.25 \\ +autoenc. & 9.97 & 1.09 & 16.92 & 23.72 \\ +priv. vars. & 9.86 & 1.70 & 16.41 & 21.75 \\ \hline TRANSIENCE-CL & **7.65** & **0.12** & 15.28 & 24.10 \\ +autoenc. & 7.76 & 0.20 & 14.98 & 24.06 \\ +priv. vars. & 7.82 & 0.30 & **14.58** & 23.68 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the objective results obtained by each alignment technique on an A2A task where speech was synthesized from PMA data captured from the lips and tongue using DNNs. The following metrics were computed between the predicted speech signals and the original speech signals recorded by the speakers: (a) speech spectral envelope distortion (MCD); (b) aperiodic component distortion (RMSE); (c) speech fundamental frequency (\(F_{0}\)) distortion (RMSE); and (d) voicing error rate (%). Best result for each set of parameters is highlighted in bold face.