# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 7 Conclusions  
In this work, we extended the quality evaluation of a voice-cloning task from a difficult corpus from our previous project [3] and compared the results on a set of two higher-quality voice cloning corpora in both English and Spanish. We first evaluated the quality of each corpora in terms of phonetic coverage, the SNR, and utterance speed. We performed an exhaustive evaluation of these features in order to detect which partitions of audio files could infer higher variability and, therefore, be detrimental to the quality of a voice-cloning model. Using these data, a set of ablations was performed on the original datasets by removing audio files that were considered of lower quality--in terms of a low SNR--or higher variability--concerning utterance speed--from the training partitions. Audio files with an SNR lower than 20 dB for the more-difficult speaker and 25 dB for the higher-quality datasets were removed. Similarly, audio files whose utterance speed was in the first and last deciles were also withdrawn from the data. In addition and since the quantities of the three corpora were not fully comparable, these same ablations were applied to a randomly chosen 3 h subset of the two corpora featuring higher quality. In this regard, a total of 20 models were trained in order to check the impact of said audio removals. In order to automatically check the quality of the trained models, we gathered a set of four different measurements. First, two different MOS estimators based on deep learning techniques were employed: NISQA [9] and MOSnet [4]. Additionally, we introduced a novel algorithm in order to complement the forced alignment of sentences. This approach takes advantage of the diagonal present in a successful synthesis in the attention matrix from the voice-cloning system Tacotron-2 in order to count the number of correctly aligned input characters. With the aid of these measurements, we proved that removing data that were considered noisier or that featured a more-variable utterance speed from the more-difficult dataset improved the overall quality of the final models when starting from a pre-trained model, even though that half of the audio files were withdrawn from training in the harshest ablation. Moreover, the estimated MOS increased around 0.2 points for both algorithms. Regarding the two datasets that featured a higher quality, since the level of noise present in these two corpora was comparatively low, the impact of only removing audio files with lower values of the SNR did not particularly help the training of the models. Nevertheless, the quality of the models with fewer data, but a more homogeneous utterance speed can be considered to be equal to those models with a higher amount of audio when using a pre-training approach in terms of the fraction of aligned characters. Finally, when training from scratch using the whole dataset, the impact of removing more variable data was seen to be negative for the speaker with a fewer number of hours in terms of the fraction of aligned characters. Regarding the estimated MOS, however, the ablations did not show any significant deterioration of the MOS values estimated by NISQA and MOSnet, not in the pre-trained framework with 3 h of audio, nor when training from scratch with the whole dataset, which means that similar quality can obtained by using much fewer training data. Concerning this point, one of the main conclusions drawn from our work is that the quality obtained by training using 3 h of data from a pretrained model--even on a different language--or by training from scratch using more voluminous datasets was relatively similar, according to the objective metrics used. These observations suggested that considerably fewer data are needed for training a voice-cloning model if provided a robust pre-training as a starting point compared to training the model from scratch with a greater amount of data, with no substantial difference in the overall obtained quality.