  There are many deterministic mathematical operations (e.g. compression,
clipping, downsampling) that degrade speech quality considerably. In this paper
we introduce a neural network architecture, based on a modification of the
DiffWave model, that aims to restore the original speech signal. DiffWave, a
recently published diffusion-based vocoder, has shown state-of-the-art
synthesized speech quality and relatively shorter waveform generation times,
with only a small set of parameters. We replace the mel-spectrum upsampler in
DiffWave with a deep CNN upsampler, which is trained to alter the degraded
speech mel-spectrum to match that of the original speech. The model is trained
using the original speech waveform, but conditioned on the degraded speech
mel-spectrum. Post-training, only the degraded mel-spectrum is used as input
and the model generates an estimate of the original speech. Our model results
in improved speech quality (original DiffWave model as baseline) on several
different experiments. These include improving the quality of speech degraded
by LPC-10 compression, AMR-NB compression, and signal clipping. Compared to the
original DiffWave architecture, our scheme achieves better performance on
several objective perceptual metrics and in subjective comparisons.
Improvements over baseline are further amplified in a out-of-corpus evaluation
setting.

Sound;Machine Learning;Audio and Speech Processing