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

Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders