  The recent emergence of machine-learning based generative models for speech
suggests a significant reduction in bit rate for speech codecs is possible.
However, the performance of generative models deteriorates significantly with
the distortions present in real-world input signals. We argue that this
deterioration is due to the sensitivity of the maximum likelihood criterion to
outliers and the ineffectiveness of modeling a sum of independent signals with
a single autoregressive model. We introduce predictive-variance regularization
to reduce the sensitivity to outliers, resulting in a significant increase in
performance. We show that noise reduction to remove unwanted signals can
significantly increase performance. We provide extensive subjective performance
evaluations that show that our system based on generative modeling provides
state-of-the-art coding performance at 3 kb/s for real-world speech signals at
reasonable computational complexity.
