  Speech codecs learn compact representations of speech signals to facilitate
data transmission. Many recent deep neural network (DNN) based end-to-end
speech codecs achieve low bitrates and high perceptual quality at the cost of
model complexity. We propose a cross-module residual learning (CMRL) pipeline
as a module carrier with each module reconstructing the residual from its
preceding modules. CMRL differs from other DNN-based speech codecs, in that
rather than modeling speech compression problem in a single large neural
network, it optimizes a series of less-complicated modules in a two-phase
training scheme. The proposed method shows better objective performance than
AMR-WB and the state-of-the-art DNN-based speech codec with a similar network
architecture. As an end-to-end model, it takes raw PCM signals as an input, but
is also compatible with linear predictive coding (LPC), showing better
subjective quality at high bitrates than AMR-WB and OPUS. The gain is achieved
by using only 0.9 million trainable parameters, a significantly less complex
architecture than the other DNN-based codecs in the literature.

Audio and Speech Processing;Machine Learning;Sound