In order to efficiently transmit and store speech signals, speech codecs
create a minimally redundant representation of the input signal which is then
decoded at the receiver with the best possible perceptual quality. In this work
we demonstrate that a neural network architecture based on VQ-VAE with a
WaveNet decoder can be used to perform very low bit-rate speech coding with
high reconstruction quality. A prosody-transparent and speaker-independent
model trained on the LibriSpeech corpus coding audio at 1.6 kbps exhibits
perceptual quality which is around halfway between the MELP codec at 2.4 kbps
and AMR-WB codec at 23.05 kbps. In addition, when training on high-quality
recorded speech with the test speaker included in the training set, a model
coding speech at 1.6 kbps produces output of similar perceptual quality to that
generated by AMR-WB at 23.05 kbps.Comment: ICASSP 201