  In recent years, supervised approaches using deep neural networks (DNNs) have
become the mainstream for speech enhancement. It has been established that DNNs
generalize well to untrained noises and speakers if trained using a large
number of noises and speakers. However, we find that DNNs fail to generalize to
new speech corpora in low signal-to-noise ratio (SNR) conditions. In this work,
we establish that the lack of generalization is mainly due to the channel
mismatch, i.e. different recording conditions between the trained and untrained
corpus. Additionally, we observe that traditional channel normalization
techniques are not effective in improving cross-corpus generalization. Further,
we evaluate publicly available datasets that are promising for generalization.
We find one particular corpus to be significantly better than others. Finally,
we find that using a smaller frame shift in short-time processing of speech can
significantly improve cross-corpus generalization. The proposed techniques to
address cross-corpus generalization include channel normalization, better
training corpus, and smaller frame shift in short-time Fourier transform
(STFT). These techniques together improve the objective intelligibility and
quality scores on untrained corpora significantly.
