Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS
Table 2: Subjective preference of models trained with different input features
['Preference (%) Baseline', 'Preference (%) Neutral', 'Preference (%) PSF', 'Preference (%) WRF', 'CMOS']
In these two test sets, syntactic feature based models have larger improvement on the complex test set. These results show that providing syntactic information can significantly help synthesize better speech, especially for sentences with more complex grammar and longer length. Our analysis of these test cases find that the improvement of the effect is mainly on the prosody.