GENERATIVE SPEECH CODING WITH PREDICTIVE VARIANCE REGULARIZATION
Abstract
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.
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.
Research Areas
