## 5 Results
The two other models, as commented upon, consistently outperform the baseline considered in all metrics and scenarios posed, with the _InterRNN_ case being the one achieving slightly better results than the _PostRNN_ one. A last point to comment upon from these figures is that, while differences between the _raw_ and _pretrained_ cases do not generally differ in a remarkable sense, convergence is always faster in the latter approach than in the former one. We now further analyze this _InterRNN_ model since, as commented upon, it is the one showing the best overall performance. To do so, we compare its performance when decoupling the predicted sequence of labels into their shape and height components against the base neural architecture when trained specifically for the \(\Sigma_{S}\) and \(\Sigma_{H}\) vocabularies. Table 4 provides the results of this analysis and reports the figures obtained for the baseline model. As it may be checked, the results comparing the _baseline_ and _best model_ architectures when decoupling the combined labels into their shape and height components depict the expected behavior: for all cases and metrics, the _best model_ decreases the error rate with respect to _baseline_. In the particular case of _Capitan_, the Symbol Error Rate decreases approximately by 2% while, for the _Il Lauro Secco_, this improvement is around 0.7% for both the shape and height individual vocabularies. The same trend is observed for the Sequence Error Rate, with the error decrease in the case of the shape vocabulary space for the _Capitan_ corpus is especially noticeable, where the improvement is around 20%. We now compare the performance of the _best model_ configuration with that of the _shape_ and _height_ models. Regarding the _Capitan_ set, the error rates achieved when decoupling the resulting predictions into the \(\Sigma_{S}\) and \(\Sigma_{H}\) vocabularies are lower than those of the actual dedicated models. These results suggest that the configuration exploits the individual sources of information in a synergistic manner that leads to such an improvement. Nonetheless, in the case of the _Il Lauro Secco_ corpus, since the _shape_ and _height_ architectures achieve better recognition rates than those of the decoupled evaluation, there is still some room for improvement, which suggests that this may have not been the best architecture for this particular set. Finally, let us comment that the narrow improvement margin that the different corpora depict must be taken into consideration. Focusing on the _Capitan_ corpus, the baseline model depicts an initial Symbol Error Rate of 10.32%, which is reduced to 7.68% with the proposed architecture. While the absolute error reduction is 2.64%, it must be noted that it supposes an improvement of 25.6% with respect to the initial figure. Similarly, the _Il Lauro Secco_ improves from an initial value of 4.87% to 4.17%, which is an absolute improvement of 0.7% but implies an error reduction of 14.4% with respect to that of the baseline model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Baseline**} & \multicolumn{3}{c}{**Best Model**} & **Shape** & **Height** \\ \cline{2-9}  & \(\mathbf{\Sigma_{T}}\) & \(\mathbf{\Sigma_{S}}\) & \(\mathbf{\Sigma_{H}}\) & \(\mathbf{\Sigma_{T}}\) & \(\mathbf{\Sigma_{S}}\) & \(\mathbf{\Sigma_{H}}\) & \(\mathbf{\Sigma_{S}}\) & \(\mathbf{\Sigma_{H}}\) \\ \hline \hline Sym-ER (\%) & & & & & & & \\ _Capitan_ & 10.32 & 7.09 & 8.84 & 7.68 & 4.57 & 6.59 & 4.92 & 7.52 \\ _Il Lauro Secco_ & 4.87 & 4.05 & 4.10 & 4.17 & 3.37 & 3.48 & 2.14 & 2.47 \\ \hline \hline Seq-ER (\%) & & & & & & & & \\ _Capitan_ & 86.53 & 77.92 & 83.75 & 77.15 & 57.05 & 72.16 & 61.57 & 78.67 \\ _Il Lauro Secco_ & 36.16 & 28.85 & 29.83 & 32.75 & 25.61 & 26.53 & 24.20 & 25.13 \\ \hline \hline \end{tabular}
\end{table}