  This paper presents a denoising and dereverberation hierarchical neural
vocoder (DNR-HiNet) to convert noisy and reverberant acoustic features into a
clean speech waveform. We implement it mainly by modifying the amplitude
spectrum predictor (ASP) in the original HiNet vocoder. This modified denoising
and dereverberation ASP (DNR-ASP) can predict clean log amplitude spectra (LAS)
from input degraded acoustic features. To achieve this, the DNR-ASP first
predicts the noisy and reverberant LAS, noise LAS related to the noise
information, and room impulse response related to the reverberation information
then performs initial denoising and dereverberation. The initial processed LAS
are then enhanced by another neural network as the final clean LAS. To further
improve the quality of the generated clean LAS, we also introduce a bandwidth
extension model and frequency resolution extension model in the DNR-ASP. The
experimental results indicate that the DNR-HiNet vocoder was able to generate a
denoised and dereverberated waveform given noisy and reverberant acoustic
features and outperformed the original HiNet vocoder and a few other neural
vocoders. We also applied the DNR-HiNet vocoder to speech enhancement tasks,
and its performance was competitive with several advanced speech enhancement
methods.
