  In this paper, we propose a deep learning (DL)-based parameter enhancement
method for a mixed excitation linear prediction (MELP) speech codec in noisy
communication environment. Unlike conventional speech enhancement modules that
are designed to obtain clean speech signal by removing noise components before
speech codec processing, the proposed method directly enhances codec parameters
on either the encoder or decoder side. As the proposed method has been
implemented by a small network without any additional processes required in
conventional enhancement systems, e.g., time-frequency (T-F) analysis/synthesis
modules, its computational complexity is very low. By enhancing the
noise-corrupted codec parameters with the proposed DL framework, we achieved an
enhancement system that is much simpler and faster than conventional T-F
mask-based speech enhancement methods, while the quality of its performance
remains similar.
