  Neural speech synthesis models can synthesize high quality speech but
typically require a high computational complexity to do so. In previous work,
we introduced LPCNet, which uses linear prediction to significantly reduce the
complexity of neural synthesis. In this work, we further improve the efficiency
of LPCNet -- targeting both algorithmic and computational improvements -- to
make it usable on a wide variety of devices. We demonstrate an improvement in
synthesis quality while operating 2.5x faster. The resulting open-source LPCNet
algorithm can perform real-time neural synthesis on most existing phones and is
even usable in some embedded devices.
