  Since its inception, the field of deep speech enhancement has been dominated
by predictive (discriminative) approaches, such as spectral mapping or masking.
Recently, however, novel generative approaches have been applied to speech
enhancement, attaining good denoising performance with high subjective quality
scores. At the same time, advances in deep learning also allowed for the
creation of neural network-based metrics, which have desirable traits such as
being able to work without a reference (non-intrusively). Since generatively
enhanced speech tends to exhibit radically different residual distortions, its
evaluation using instrumental speech metrics may behave differently compared to
predictively enhanced speech. In this paper, we evaluate the performance of the
same speech enhancement backbone trained under predictive and generative
paradigms on a variety of metrics and show that intrusive and non-intrusive
measures correlate differently for each paradigm. This analysis motivates the
search for metrics that can together paint a complete and unbiased picture of
speech enhancement performance, irrespective of the model's training process.
