  The increasingly stringent requirement on quality-of-experience in 5G/B5G
communication systems has led to the emerging neural speech enhancement
techniques, which however have been developed in isolation from the existing
expert-rule based models of speech pronunciation and distortion, such as the
classic Linear Predictive Coding (LPC) speech model because it is difficult to
integrate the models with auto-differentiable machine learning frameworks. In
this paper, to improve the efficiency of neural speech enhancement, we
introduce an LPC-based speech enhancement (LPCSE) architecture, which leverages
the strong inductive biases in the LPC speech model in conjunction with the
expressive power of neural networks. Differentiable end-to-end learning is
achieved in LPCSE via two novel blocks: a block that utilizes the expert rules
to reduce the computational overhead when integrating the LPC speech model into
neural networks, and a block that ensures the stability of the model and avoids
exploding gradients in end-to-end training by mapping the Linear prediction
coefficients to the filter poles. The experimental results show that LPCSE
successfully restores the formants of the speeches distorted by transmission
loss, and outperforms two existing neural speech enhancement methods of
comparable neural network sizes in terms of the Perceptual evaluation of speech
quality (PESQ) and Short-Time Objective Intelligibility (STOI) on the LJ Speech
corpus.
