Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS
Table 1: Subjective evaluations on different layers and orders. T: top-down, B: bottom-up, e.g. T5 refers to top-down 5 levels
['Preference (%) Feature A', 'Preference (%) Feature A', 'Preference (%) Neutral', 'Preference (%) Feature B', 'Preference (%) Feature B', 'CMOS']
The comparisons show most models achieve similar performance. But their good effect are performed on different sentences. For example, the comparison of T5 and T10 show a small CMOS score of 0.03. But the preferences on these two models are high and similar. It shows that the two features have their own unique effects on different sentences. So we infer that selection of different layers may lead to different performance on the same sentence. Finally we sort these models by CMOS scores and preference, then adopt T10 as phrase structure based features (PSF).