  Recent research on speech enhancement (SE) has seen the emergence of deep
learning-based methods. It is still a challenging task to determine effective
ways to increase the generalizability of SE under diverse test conditions. In
this paper, 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 two phases, namely 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 carry out enhancement. Two selection strategies are developed:
selection based on quality score (QS) and selection based on quality embedding
(QE). Both QS and QE are obtained by a Quality-Net, a non-intrusive quality
assessment network. In the offline phase, the QS or QE of a train-ing utterance
is used to group the training data into clusters. In the online phase, the QS
or QE of the test utterance is used to identify the appropriate component SE
model to perform enhancement on the test utterance. Experimental results have
confirmed that the proposed ZMOS approach can achieve better performance in
both seen and unseen noise types compared to the baseline systems, which
indicates the effectiveness of the proposed approach to provide robust SE
performance.
