## 4 Experiments and Results
### Speech Generation  
To measure the perceptual performance of the models, the generation of the synthetic speech is required. We employed the FF-BLSTM model to predict spectral features based on [11]. The model was trained by using the input linguistic features adopted from the question set for training the model for HMM-based speech synthesis for Isarn [5]. The input feature vector consisted of 489-dimensional linguistic features: 472 dimensions of categorical linguistic contexts (e.g., phonemes identities, tone of syllable), 14 dimensions of numerical linguistic contexts (e.g., position of the current syllable in the current word, number of syllables in the current word), and 3 dimensions of frame-level features.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**K (Frames)** & **RMSE (Hz)** & **CORR** \\ \hline
10 & 8.320 \(\pm\) 2.86 & 0.927 \(\pm\) 0.04 \\
20 & 8.242 \(\pm\) 2.76 & 0.927 \(\pm\) 0.04 \\
30 & 8.202 \(\pm\) 2.79 & 0.928 \(\pm\) 0.04 \\
40 & 8.201 \(\pm\) 2.76 & 0.927 \(\pm\) 0.04 \\
45 & 8.172 \(\pm\) 2.65 & 0.927 \(\pm\) 0.04 \\
50 & 8.243 \(\pm\) 2.79 & 0.927 \(\pm\) 0.04 \\
60 & 8.315 \(\pm\) 2.81 & 0.926 \(\pm\) 0.04 \\
80 & 8.328 \(\pm\) 2.65 & 0.927 \(\pm\) 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Objective results of the training of the proposed model with different values of \(K\).  
Figure 5: Distribution of syllable duration in Isarn speech corpus.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model Name** & **Feature Set** & **RMSE (Hz)** & **CORR** \\ \hline PH & only phone features & 11.395 \(\pm\) 3.29 & 0.824 \(\pm\) 0.12 \\ PH\_TN & phone + tone features & 8.825 \(\pm\) 2.92 & 0.916 \(\pm\) 0.04 \\ PH\_TN\_PS & phone + tone + positional features & 8.778 \(\pm\) 2.85 & 0.919 \(\pm\) 0.04 \\ TN\_PS\_DU & tone + positional + duration features & 8.657 \(\pm\) 2.69 & 0.918 \(\pm\) 0.04 \\ PH\_TN\_PS\_DU & all features & 8.172 \(\pm\) 2.65 & 0.927 \(\pm\) 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The RMSE and correlation (CORR) of models trained by different combinations of feature sets (\(\pm\)denotes standard deviation).  
The frame-level input features were considered, including forward/backward positions of the given frame in the given phone and phone duration. The output feature vector comprised 196-dimensional acoustic features containing the 60-dimensional Mel-cepstral coefficients, 4-dimensional band aperiodicies, and \(\log F_{0}\) with their dynamic features and voiced/unvoiced flags. Similar to [11], we included all silence frames for training to preserve the continuity of acoustic features within a sentence. The input and output features were normalized to have zero mean and unit variance. We used the network structure of three feedforward layers, with 512 nodes per layer, where the top two hidden layers had a BLSTM structure. Each combined 128 forward and 128 backward nodes. This model was used to generate the speech parameters for all experiments.  
In the synthesis stage, the sequence of input feature vectors was fed to the trained RNN-based acoustic model to produce the speech parameters. Then, these speech parameters were smoothened by using a parameter generation algorithm [52]. Following this, the output speech parameters were enhanced using a post-filtering algorithm [57] to improve the naturalness of the synthetic speech. Finally, the waveform of speech was generated through the speech vocoder.