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arXiv · 1910.06464

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
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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.

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Cristina Gârbacea, Aäron van den Oord, Yazhe Li, Felicia S C Lim, Alejandro Luebs, Oriol Vinyals, Thomas C Walters. 2019-10-14. Low Bit-Rate Speech Coding with VQ-VAE and a WaveNet Decoder. https://doi.org/10.1109/icassp.2019.8683277

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