# Evaluation of Tacotron Based Synthesizers for Spanish and Basque
## 2 Materials and Methods
### Training Dataset  
Every neural network based system completely depends on the data used to train it. For the TTS task it is necessary to have a speech corpus with its corresponding orthographic transcriptions. On the one hand, we used two phonetically balanced datasets of around 4100 utterances each, one in Basque and one in Spanish. These corpora had been recorded in the premises of our laboratory by one female and one male bilingual speakers. The datasets are composed by utterances with an average of \(10.10\pm 2.81\) and \(12.84\pm 3.93\) words per utterance in Basque and Spanish, respectively. The duration of the speech signals in each corpus amounts to approximately 4 h 30 min.  
On the other hand, and as an extension to the work presented in [20], we included an additional Spanish dataset. This addition is intended to evaluate the performance of the system when trained on a larger corpus with different sentence length distributions. The corpus was provided by the Blizzard Challenge Organisation and corresponds to the dataset supplied to the participants of the Blizzard Challenge 2021 ([[https://www.synsig.org/index.php/Blizzard_Challenge_2021](https://www.synsig.org/index.php/Blizzard_Challenge_2021)]([https://www.synsig.org/index.php/Blizzard_Challenge_2021](https://www.synsig.org/index.php/Blizzard_Challenge_2021)) (accessed on 20 December 2021)). The data consists of 5.25 h of high quality audio recordings by a single female speaker, including the corresponding 4920 orthographic transcriptions.  
Table 1 shows a summary of the most relevant details regarding the structure of the training datasets used in this work. It can be seen that despite having less lexical variety, the utterances in the Spanish Blizzard corpus have a wider range of lengths than in the previous Spanish corpus. This detail can also be observed in Figure 1, where the distribution of the sentence lengths across all datasets is shown.  
Before training the models, both the text and the speech signals were processed. The processing of the transcriptions was performed by means of a Spanish and Basque linguistic Front-End [21]. This Front-End is composed by two main modules: a text processor and a linguistic processor. The text processor normalizes the input text by expanding acronyms and converting numbers into directly readable words. The output of this module is directly fed into the linguistic processor, which returns the SAMPA [22] phone sequence along with the stress level of each phone.  
Regarding the audio, tail silences were reduced to 150 ms and silences at the beginning of the utterances were completely taken out, as this process eases the learning of the alignment between text and audio. We decimated the original sampling frequency from 48 to 22.05 KHz to correspond the sample rate of a pre-trained model that would be later adapted using this corpus.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Spanish** & **Basque** & **Spanish Blizzard** \\ \hline Number of utterances & 3993 & 3795 & 4920 \\ Number of words & 51,301 & 38,339 & 50,732 \\ Unique words & 10,687 & 12,352 & 8661 \\ Words in shortest utterance & 4 & 4 & 1 \\ Words in longest utterance & 26 & 19 & 50 \\ Avg. words per utterance & \(12.84\pm 3.93\) & \(10.10\pm 2.81\) & \(10.30\pm 3.88\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the training datasets.  
Figure 1: Distribution of words per utterance in the training dataset.  
An additional processing step was performed to the Spanish Blizzard corpus. Inspection of the audio files revealed some misalignment issues between the pauses indicated by punctuation signs in the transcriptions and the actual pauses of the speech signals. This misalignment issue harms the training of the models, so we automatically re-positioned the pauses in the phoneme sequences using the actual silences in the audio signals as reference.