  In this work, we propose the use of dropouts as a Bayesian estimator for
increasing the generalizability of a deep neural network (DNN) for speech
enhancement. By using Monte Carlo (MC) dropout, we show that the DNN performs
better enhancement in unseen noise and SNR conditions. The DNN is trained on
speech corrupted with Factory2, M109, Babble, Leopard and Volvo noises at SNRs
of 0, 5 and 10 dB and tested on speech with white, pink and factory1 noises.
Speech samples are obtained from the TIMIT database and noises from NOISEX-92.
In another experiment, we train five DNN models separately on speech corrupted
with Factory2, M109, Babble, Leopard and Volvo noises, at 0, 5 and 10 dB SNRs.
The model precision (estimated using MC dropout) is used as a proxy for squared
error to dynamically select the best of the DNN models based on their
performance on each frame of test data.
