Prosody Transfer in Neural Text to Speech Using Global Pitch and Loudness Features
Table 2: Objective metrics for prosody transfer. Lower is better.
['Distance Metric', 'TC2', 'GS-TC2']
In this experiment, for each text in the test set (consisting of 100 examples), a reference is selected uniformly at random from a set of 100 references that contain non-neutral prosodies and the text is synthesized using the GS-TC2 model, conditioned on that reference. We conducted 50 Monte Carlo runs in all cases. It can be seen that GS-TC2 can synthesize speech with GS features closer to the ground-truth reference audio compared to baseline TC2. It can also be observed that GS-TC2 is more successful in transferring the reference pitch contour information to the synthesized speech compared to TC2, whereas, in terms of RMS, the DTW distance is similar for GS-TC2 and TC2.