  Traditional low bit-rate speech coding approach only handles narrowband
speech at 8kHz, which limits further improvements in speech quality. Motivated
by recent successful exploration of deep learning methods for image and speech
compression, this paper presents a new approach through vector quantization
(VQ) of mel-frequency cepstral coefficients (MFCCs) and using a deep generative
model called WaveGlow to provide efficient and high-quality speech coding. The
coding feature is sorely an 80-dimension MFCCs vector for 16kHz wideband
speech, then speech coding at the bit-rate throughout 1000-2000 bit/s could be
scalably implemented by applying different VQ schemes for MFCCs vector. This
new deep generative network based codec works fast as the WaveGlow model
abandons the sample-by-sample autoregressive mechanism. We evaluated this new
approach over the multi-speaker TIMIT corpus, and experimental results
demonstrate that it provides better speech quality compared with the
state-of-the-art classic MELPe codec at lower bit-rate.
