Low Bit-rate Speech Coding with VQ-VAE and a WaveNet Decoder
Cristina Gârbacea, Aäron van den Oord, Yazhe Li, Felicia S. C. Lim, Alejandro Luebs, Oriol Vinyals, Thomas C. Walters
Abstract
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.
BibTeX
@inproceedings{icassp2019_lowbitratespeech,
title = {Low Bit-rate Speech Coding with VQ-VAE and a WaveNet Decoder},
author = {Cristina Gârbacea and Aäron van den Oord and Yazhe Li and Felicia S. C. Lim and Alejandro Luebs and Oriol Vinyals and Thomas C. Walters},
booktitle = {ICASSP 2019},
year = {2019}
}