  As deep speech enhancement algorithms have recently demonstrated capabilities
greatly surpassing their traditional counterparts for suppressing noise,
reverberation and echo, attention is turning to the problem of packet loss
concealment (PLC). PLC is a challenging task because it not only involves
real-time speech synthesis, but also frequent transitions between the received
audio and the synthesized concealment. We propose a hybrid neural PLC
architecture where the missing speech is synthesized using a generative model
conditioned using a predictive model. The resulting algorithm achieves natural
concealment that surpasses the quality of existing conventional PLC algorithms
and ranked second in the Interspeech 2022 PLC Challenge. We show that our
solution not only works for uncompressed audio, but is also applicable to a
modern speech codec.
