  This work proposes the use of clean speech vocoder parameters as the target
for a neural network performing speech enhancement. These parameters have been
designed for text-to-speech synthesis so that they both produce high-quality
resyntheses and also are straightforward to model with neural networks, but
have not been utilized in speech enhancement until now. In comparison to a
matched text-to-speech system that is given the ground truth transcripts of the
noisy speech, our model is able to produce more natural speech because it has
access to the true prosody in the noisy speech. In comparison to two denoising
systems, the oracle Wiener mask and a DNN-based mask predictor, our model
equals the oracle Wiener mask in subjective quality and intelligibility and
surpasses the realistic system. A vocoder-based upper bound shows that there is
still room for improvement with this approach beyond the oracle Wiener mask. We
test speaker-dependence with two speakers and show that a single model can be
used for multiple speakers.
