# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 5 Experimental Framework
### Postprocessed Datasets  
As was claimed before, one of the key aspects of training a voice-cloning system based on a monolingual TTS approach is the homogeneity of the data. In order to reduce the variability of the gathered corpora, three main decisions were made within the data-selection phase:  
1. Removing the sentences whose SNR value was lower than 20 dB in order to ensure a sufficient signal quality. A value of 25 dB was chosen for the HQ datasets since the quality of these corpora was notably higher (refer to Figure 2 and Table 1).
2. Removing the sentences whose utterance speed value was inside the first or last deciles. More specifically, maintaining audio files that \(14.17<S<16.88\) for _XRev_, \(13.16<S<16.75\) for _Tux_, and \(10.27<S<14.06\) for _Hi-Fi TTS 92_, where \(S\) is obtained from Equation (1) (refer to Figure 3 and Table 2).
3. Only audio files whose duration was between 1 and 10 s were used in order to reduce variability and increase the batch size during training.  
Due to the lack of data for corpus _XRev_ and the particularly long utterances of its speaker, the original audio files were divided based on pauses found inside sentences instead of discarding whole utterances in order to ensure that a significant part of the dataset was not lost.  
With regard to the HQ datasets, in order to reproduce the difficult conditions in terms of the quantity of data found for _XRev_, a random partition of 3 h of audio was chosen from each HQ dataset, composed of 1 h of audio from the ranges of 1 to 3 s, 3 to 7 s, and 7 to 10 s. These datasets were also reduced in terms of the SNR and utterance speed. This process resulted in a total of 20 different audio collections shown in Table 3.  
The impact of these ablations was measured with multiple trainings of the voice-cloning systems as explained in Section 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Speaker**} & \multicolumn{2}{c}{**All**} & \multicolumn{2}{c}{**High SNR 1**} & \multicolumn{2}{c}{**Utt. Speed 2**} & \multicolumn{2}{c}{**SNR and Speed 1.2**} \\  & **Files** & **Hours** & **Files** & **Hours** & **Files** & **Hours** & **Files** & **Hours** \\ \hline XRev & 1075 & 3:13 & 588 & 1:49 & 792 & 2:25 & 493 & 1:33 \\ Short sentences 3 & 2398 & 2:46 & 1451 & 1:35 & 1978 & 2:08 & 1249 & 1:23 \\ \hline Tux _valid_ & 52,398 & 53:46 & 44,549 & 47:21 & 38,395 & 44:16 & 33,889 & 40:33 \\ Short sentences 3 & 46,846 & 45:08 & 40,111 & 39:43 & 35,503 & 36:46 & 31,345 & 33:31 \\
3 h partition & 3092 & 3:00 & 2649 & 2:39 & 2326 & 2:27 & 2061 & 2:15 \\ \hline Hi-Fi TTS 92 & 35,296 & 27:18 & 31,634 & 25:02 & 25,975 & 21:40 & 23,996 & 20:16 \\ Short sentences 3 & 33,589 & 26:34 & 30,374 & 24:27 & 25,838 & 21:19 & 23,889 & 19:58 \\
3 h partition & 3131 & 3:00 & 2835 & 2:45 & 2486 & 2:31 & 2301 & 2:21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the number of files and hours of _XRev_, _Tux_, and speaker 92 of _Hi-Fi TTS_ after removing audio files considered to increase variability. Superscripts 1, 2, and 3 correspond to the changes proposed in Section 5.1. The reader may note that the files of speaker _XRev_ were divided in order to obtain shorter audio files.