  Self-supervised learning (SSL) achieves great success in monaural speech
enhancement, while the accuracy of the target speech estimation, particularly
for unseen speakers, remains inadequate with existing pre-tasks. As speech
signal contains multi-faceted information including speaker identity,
paralinguistics, and spoken content, the latent representation for speech
enhancement becomes a tough task. In this paper, we study the effectiveness of
each feature which is commonly used in speech enhancement and exploit the
feature combination in the SSL case. Besides, we propose an ensemble training
strategy. The latent representation of the clean speech signal is learned,
meanwhile, the dereverberated mask and the estimated ratio mask are exploited
to denoise and dereverberate the mixture. The latent representation learning
and the masks estimation are considered as two pre-tasks in the training stage.
In addition, to study the effectiveness between the pre-tasks, we compare
different training routines to train the model and further refine the
performance. The NOISEX and DAPS corpora are used to evaluate the efficacy of
the proposed method, which also outperforms the state-of-the-art methods.
