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Recently, GAN vocoders have seen rapid progress in speech synthesis, startingto outperform autoregressive models in perceptual quality with much highergeneration speed. However, autoregressive vocoders are still the common choicefor neural generation of speech signals coded at very low bit rates. In thispaper, we present a GAN vocoder which is able to generate wideband speechwaveforms from parameters coded at 1.6 kbit/s. The proposed model is a modifiedversion of the StyleMelGAN vocoder that can run in frame-by-frame manner,making it suitable for streaming applications. The experimental results showthat the proposed model significantly outperforms prior autoregressive vocoderslike LPCNet for very low bit rate speech coding, with computational complexityof about 5 GMACs, providing a new state of the art in this domain. Moreover,this streamwise adversarial vocoder delivers quality competitive to advancedspeech codecs such as EVS at 5.9 kbit/s on clean speech, which motivatesfurther usage of feed-forward fully-convolutional models for low bit ratespeech coding.

A Streamwise GAN Vocoder for Wideband Speech Coding at Very Low Bit Rate