  Audio codecs based on discretized neural autoencoders have recently been
developed and shown to provide significantly higher compression levels for
comparable quality speech output. However, these models are tightly coupled
with speech content, and produce unintended outputs in noisy conditions. Based
on VQ-VAE autoencoders with WaveRNN decoders, we develop compressor-enhancer
encoders and accompanying decoders, and show that they operate well in noisy
conditions. We also observe that a compressor-enhancer model performs better on
clean speech inputs than a compressor model trained only on clean speech.
