Investigating accuracy of pitch-accent annotations in neural network-based speech synthesis and denoising effects
Table 1: Definition and notations of TTS systems used in this experiment. Definition of linguistic feature sets is given in Section 4.1. Oracle + Corrupted means that 7999 randomly selected training utterances used corrupted labels, while rest used oracle linguistic features. Also shown are RMSE values and correlation of generated F0 and U/UV errors compared with natural speech. Mel-cepstral distortion (MCD) between generated and natural MGCs is also shown.
['Notations', 'Linguistic features Train set', 'Linguistic features Test set', 'Objective measures RMSE', 'Objective measures CORR', 'Objective measures V/U Error', 'Objective measures MCD [dB]']
To investigate the impact of the noise in linguistic features, we trained a few systems by using different sets of linguistic features in the training and test stages as we described earlier. Note that the linguistic features for the validation set were not corrupted. Also note that, instead of using the results of our previous study, we retrained the OpenJTalk-based model (OJT) by using the same data set configuration described in Section 4.1. Thus, the results of OJT can be compared with those of other experimental models. As expected, the model trained and tested by using manually annotated labels, i.e., MOO, achieved the best results among the systems for all of the measurements. We can also see that when testing on the corrupted linguistic labels, the performance of MOC drastically dropped.