  The current dominant approach for neural speech enhancement is via
purely-supervised deep learning on simulated pairs of far-field
noisy-reverberant speech (i.e., mixtures) and clean speech. The trained models,
however, often exhibit limited generalizability to real-recorded mixtures. To
deal with this, this paper investigates training enhancement models directly on
real mixtures. However, a major difficulty challenging this approach is that,
since the clean speech of real mixtures is unavailable, there lacks a good
supervision for real mixtures. In this context, assuming that a training set
consisting of real-recorded pairs of close-talk and far-field mixtures is
available, we propose to address this difficulty via close-talk speech
enhancement, where an enhancement model is first trained on simulated mixtures
to enhance real-recorded close-talk mixtures and the estimated close-talk
speech can then be utilized as a supervision (i.e., pseudo-label) for training
far-field speech enhancement models directly on the paired real-recorded
far-field mixtures. We name the proposed system $\textit{ctPuLSE}$. Evaluation
results on the CHiME-4 dataset show that ctPuLSE can derive high-quality
pseudo-labels and yield far-field speech enhancement models with strong
generalizability to real data.
