Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS
Table 3: Diagnostic intelligibility and naturalness test without using SUS sentences on the pathological test set
['Diagnostic Intelligibility/Naturalness Rate (%) Case Level', 'Diagnostic Intelligibility/Naturalness Rate (%) Baseline', 'Diagnostic Intelligibility/Naturalness Rate (%) With PSF', 'Diagnostic Intelligibility/Naturalness Rate (%) With WRF']
Around 10% of results synthesized by baseline model have intelligibility or naturalness issues. When the models are trained with the syntactic features, the troublesome issues are significantly improved. It shows that syntactic information is helpful to improve the pronunciation clarity and generalization capability. WRF still yields the best performance, in comparing with PSF or the baseline system as shown in the Table.