  Packet loss is a common problem in data transmission, including speech data
transmission. This may affect a wide range of applications that stream audio
data, like streaming applications or speech emotion recognition (SER). Packet
Loss Concealment (PLC) is any technique of facing packet loss. Simple PLC
baselines are 0-substitution or linear interpolation. In this paper, we present
a concealment wrapper, which can be used with stacked recurrent neural cells.
The concealment cell can provide a recurrent neural network (ConcealNet), that
performs real-time step-wise end-to-end PLC at inference time. Additionally,
extending this with an end-to-end emotion prediction neural network provides a
network that performs SER from audio with lost frames, end-to-end. The proposed
model is compared against the fore-mentioned baselines. Additionally, a
bidirectional variant with better performance is utilised. For evaluation, we
chose the public RECOLA dataset given its long audio tracks with continuous
emotion labels. ConcealNet is evaluated on the reconstruction of the audio and
the quality of corresponding emotions predicted after that. The proposed
ConcealNet model has shown considerable improvement, for both audio
reconstruction and the corresponding emotion prediction, in environments that
do not have losses with long duration, even when the losses occur frequently.
