This new demo presents LPCNet, an architecture that combines signal processing and deep learning to improve the efficiency of neural speech synthesis. Neural speech synthesis models like WaveNet have recently demonstrated impressive speech synthesis quality. Unfortunately, their computational complexity has made them hard to use in real-time, especially on phones. As was the case in the RNNoise project, one solution is to use a combination of deep learning and digital signal processing (DSP) techniques. This demo explains the motivations for LPCNet, shows what it can achieve, and explores its possible applications.