  Neural vocoders have recently demonstrated high quality speech synthesis, but
typically require a high computational complexity. LPCNet was proposed as a way
to reduce the complexity of neural synthesis by using linear prediction (LP) to
assist an autoregressive model. At inference time, LPCNet relies on the LP
coefficients being explicitly computed from the input acoustic features. That
makes the design of LPCNet-based systems more complicated, while adding the
constraint that the input features must represent a clean speech spectrum. We
propose an end-to-end version of LPCNet that lifts these limitations by
learning to infer the LP coefficients from the input features in the frame rate
network. Results show that the proposed end-to-end approach equals or exceeds
the quality of the original LPCNet model, but without explicit LP analysis. Our
open-source end-to-end model still benefits from LPCNet's low complexity, while
allowing for any type of conditioning features.
