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

Neural Speech Synthesis on a Shoestring: Improving the Efficiency of LPCNet