  Recent research on speech enhancement (SE) has seen the emergence of
deep-learning-based methods. It is still a challenging task to determine the
effective ways to increase the generalizability of SE under diverse test
conditions. In this study, we combine zero-shot learning and ensemble learning
to propose a zero-shot model selection (ZMOS) approach to increase the
generalization of SE performance. The proposed approach is realized in the
offline and online phases. The offline phase clusters the entire set of
training data into multiple subsets and trains a specialized SE model (termed
component SE model) with each subset. The online phase selects the most
suitable component SE model to perform the enhancement. Furthermore, two
selection strategies were developed: selection based on the quality score (QS)
and selection based on the quality embedding (QE). Both QS and QE were obtained
using a Quality-Net, a non-intrusive quality assessment network. Experimental
results confirmed that the proposed ZMOS approach can achieve better
performance in both seen and unseen noise types compared to the baseline
systems and other model selection systems, which indicates the effectiveness of
the proposed approach in providing robust SE performance.
