# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 6 Evaluation Results and Discussion
### Evaluation of HQ Speakers Trained on the Whole Corpora  
Subsequently, the evaluation results derived from the eight models that were trained using both the complete corpora and their respective ablations will be presented, as detailed in Section 5.1.  
The metrics regarding the two MOS estimators NISQA and MOSnet on the eight different trainings corresponding to the corpora derived from the whole datasets are displayed in Figure 8.  
One of the key findings derived from the MOS estimations obtained through NISQA and MOSnet is that the trends and values observed in the advanced stages of training were remarkably similar between models trained with random 3 h partitions and those trained with the entire corpus. Notably, Speaker _Tux_ achieved values close to 3.0 for NISQA and 2.5 for MOSnet, while _Hi-Fi TTS 92_ attained higher values of 3.3 for NISQA and 3.1 for MOSnet. These experiments suggested that similar quality can be achieved when training a 3 h corpus from a pre-trained model compared to a more-voluminous corpus trained from scratch, at least based on these MOS estimators.  
Finally, the information regarding the fraction of aligned characters and sentences is presented in Figure 9.  
Similar to the previous examples, once the fraction of aligned sentences approached the maximum value of one in the later stages of training, this particular metric had no substantial information regarding the quality of the synthesised utterances.  
Figure 7: Fraction of aligned characters (**above**) and sentences (**below**) for generated audios for each iteration for the HQ speakers _[PERSON]_ (**left**) and _Hi-Fi TTS 92_ (**right**) on the random 3 h partitions.  
Concerning the fraction of aligned characters, however, two contrary trends can be witnessed for the two speakers. In the case of _[PERSON]_, the performed ablations had a positive impact on the fraction of aligned input characters, even in the earlier stages of training. In relation to the speaker _Hi-Fi TTS 92_, however, the opposite holds true, since the removal of data that were considered of higher variability negatively affected this particular metric. This discrepancy may arise from the notable disparity in the training data between the two corpora, since speaker _[PERSON]_ possessed a considerably larger number of audio hours compared to _Hi-Fi TTS 92_. Consequently, it became more feasible for _[PERSON]_ than for _Hi-Fi TTS 92_ to exclude more challenging audio files from the training process without compromising the quality of the final model, at least as this metric is concerned.  
Figure 8: Estimated MOS values obtained from NISQA (**above**) and MOSnet (**below**) for the audio generated for each iteration for the HQ speakers _[PERSON]_ (**left**) and _Hi-Fi TTS 92_ (**right**) trained from scratch on the whole corpora.  
Figure 9: Fraction of aligned characters (**above**) and sentences (**below**) for the audio generated for each iteration for the HQ speakers _[PERSON]_ (**left**) and _Hi-Fi TTS 92_ (**right**) trained from scratch on the whole corpora.