  Packet loss concealment (PLC) is challenging in concealing missing contents
both plausibly and naturally when there are only limited available context to
use. Recently deep-learning based PLC algorithms have demonstrated their
superiority over traditional counterparts; but their concealment ability is
still mostly limited to a maximum of 120ms loss. Even with strong GAN-based
generative models, it is still very challenging to predict long burst losses
that could happen within/in-between phonemes. In this paper, we propose to use
contrastive learning to learn a loss-robust semantic representation for PLC. A
hybrid neural PLC architecture combining the semantic prediction and GAN-based
generative model is designed to verify its effectiveness. Results on the blind
test set of Interspeech2022 PLC Challenge show its superiority over commonly
used UNet-style framework and the one without contrastive learning, especially
for the longer burst loss at (120, 220] ms.
