A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet
Abstract
Neural speech synthesis algorithms are a promising new approach for coding speech at very low bitrate. They have so far
demonstrated quality that far exceeds traditional vocoders, at
the cost of very high complexity. In this work, we present a low bit rate neural vocoder based on the LPCNet model. The use of
linear prediction and sparse recurrent networks makes it possible to achieve real-time operation on general-purpose hardware.
We demonstrate that LPCNet operating at 1.6 kb/s achieves
significantly higher quality than MELP and that uncompressed
LPCNet can exceed the quality of a waveform codec operating
at low bitrate. This opens the way for new codec designs based
on neural synthesis models.