WaveNODE: A Continuous Normalizing Flow for Speech Synthesis
Table 1: Mean opinion scores (MOS) with 95% confidence intervals and conditional log-likelihoods (CLL) on the test set. The CLL obtained by solving neural ODEs is labeled with †.
['Model', 'Number of parameters', 'CLL', 'MOS']
In both the MOS and the CLL tests, the performance of the WaveNet vocoder was the best among all vocoders used in the experiments. Among the flow-based vocoder models, WaveGlow gained the highest scores for both MOS and CLL of 4.17 and 4.501, respectively. The MOS score of WaveNODE was between the scores of WaveGlow and FloWaveNet. Note that WaveGlow and FloWaveNet have a much larger number of parameters than WaveNODE. To verify that only WaveNODE is capable of generating high-fidelity audio with a few flow steps, we also evaluated the performance of the compressed models of WaveGlow and FloWaveNet. The results demonstrate that the ability of WaveNODE to use constraint-free functions for flow transformation allows the implementation of a memory-efficient vocoder that is capable of generating high-fidelity waveforms.