# Exploring Efficient Neural Architectures for Linguistic-Acoustic Mapping in Text-To-Speech +
## 6 Results  
Table 2 shows the objective results for each of the systems detailed in Section 5.3. These scores were extracted over the two mentioned speakers, M and F. For both speakers, it can be seen how RNN models and QLAD models fall within a comparable range in all metrics for both speakers and model sizes. On the contrary, SALAD models are always behind the two former ones. There is an expectable improvement trend when going from each small model to its big version by increasing the number of parameters. As such, the best models happen to be the big ones, although the one for which the gap is larger is for SALAD models. This might imply that the sequential task might be much more difficult when recurrent connections are missing, and much more capacity is needed to close the gap towards the recurrent models, at least with the reduced training set we used. Nonetheless, the smallest gap from SALAD to RNN, occurring with the biggest models respectively, is 0.3 dB in the case of the male speaker and 0.06 dB in the case of the female speaker, showing the competitive performance of these non-recurrent structures objectively. Figure 2 depicts the evolution of test distortions with respect to the available training set size for each model and speaker. Each training set portion is shown in percentage, such that 5% means 5 min of training data. We can observe how SALAD struggles in the low data regimes, where only 5%, 25% or 50% of the training set is available. This indicates that it might reach a more comparable performance if we used even more training data. Nonetheless, the recurrent-connected architectures are a better fit for these low data regimes, as they can potentially capture the sequential nature of the acoustic data easily by carrying the cell states forward. In Table 3, we show the results for the subjective evaluation. Albeit objective results show comparable performance for QLAD and SALAD models with respect to RNNs, we can see remarkable evidence in the subjective results determining SALAD to yield a worse performing synthesis in terms of naturalness. The SALAD MOS degradation is potentially provoked by mistakes in the predicted temporal dynamics of the acoustic contours, as we can easily hear prosodic defects or bad intelligible regions in a preliminary listening test. In addition, the non-recurrent output layer degrades frame transition quality, even when positioning codes are available. On the other hand, the big QLAD model is very close (if not equivalent) to the big RNN one. The recurrent structure of QRNNs is then an important ingredient to match the RNN performance, but pre-projecting the activations all at once for all time-steps is also possible to speed up the synthesis and not degrade performance. Qualitative results in the form of audio samples are also available online ([[http://veu.talp.cat/efftts/](http://veu.talp.cat/efftts/)]([http://veu.talp.cat/efftts/](http://veu.talp.cat/efftts/))). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **\#Params** & **MCD [dB]** & **F0 [Hz]** & **Err [\%]** \\ \hline Small RNN & 1.17 M & 5.18 & 13.64 & 5.1 \\ Small QLAD & 1.01 M & 5.18 & 13.95 & 5.2 \\ Small SALAD & 1.04 M & 5.92 & 16.33 & 6.2 \\ Big RNN & 9.85 M & 5.15 & 13.58 & 5.1 \\ Big QLAD & 10.04 M & 5.15 & 13.77 & 5.2 \\ Big SALAD & 9.66 M & 5.43 & 14.56 & 5.5 \\ \hline Small RNN & 1.17 M & 4.63 & 15.11 & 3.2 \\ Small QLAD & 1.01 M & 4.86 & 16.66 & 3.8 \\ Small SALAD & 1.04 M & 5.25 & 20.15 & 3.6 \\ Big RNN & 9.85 M & 4.73 & 15.44 & 3.1 \\ Big QLAD & 10.04 M & 4.62 & 16.00 & 3.3 \\ Big SALAD & 9.66 M & 4.79 & 16.02 & 3.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Male (top) and female (bottom) objective results. Err, unvoiced–voiced classification error. We can see the inference speed profile of each system in Figure 3. These inference speeds are shown for the different big models depending on the synthesized waveform length, for both CPU and GPU systems. Each dot represents an utterance inference at a certain utterance length.