  In this work, we propose the use of dropout 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. 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. We
propose an algorithm with a threshold on the model precision to switch between
classifier based model selection scheme and model precision based selection
scheme. Testing is done on speech corrupted with unseen noises White, Pink and
Factory1 and all five seen noises.
