WaveNODE: A Continuous Normalizing Flow for Speech Synthesis
Table 3: Evaluations on mean opinion score (MOS) and conditional log-likelihood (CLL) for WaveNODE models with different types of norm layer.
['Model', 'Norm layer', 'CLL', 'MOS']
The results show that the norm layer is advantageous for WaveNODE to achieve good performance when adopting a multi-scale architecture unlike WaveGlow which can be fully trained without batch normalization. In our experiments, WaveNODE showed the finest results in terms of both MOS and CLL when employing an actnorm layer rather than the MBN layer. This implies that CNF-based models for speech data can be trained more efficiently by exploiting the actnorm layer.