  Neural network based approaches to speech enhancement have shown to be
particularly powerful, being able to leverage a data-driven approach to result
in a significant performance gain versus other approaches. Such approaches are
reliant on artificially created labelled training data such that the neural
model can be trained using intrusive loss functions which compare the output of
the model with clean reference speech. Performance of such systems when
enhancing real-world audio often suffers relative to their performance on
simulated test data. In this work, a non-intrusive multi-metric prediction
approach is introduced, wherein a model trained on artificial labelled data
using inference of an adversarially trained metric prediction neural network.
The proposed approach shows improved performance versus state-of-the-art
systems on the recent CHiME-7 challenge \ac{UDASE} task evaluation sets.
