  In this report we described our approach achieves $53\%$ of unweighted
accuracy over $7$ emotions and $0.05$ and $0.09$ mean squared errors for
arousal and valence in OMG emotion recognition challenge. Our results were
obtained with ensemble of single modality models trained on voice and face data
from video separately. We consider each stream as a sequence of frames. Next we
estimated features from frames and handle it with recurrent neural network. As
audio frame we mean short $0.4$ second spectrogram interval. For features
estimation for face pictures we used own ResNet neural network pretrained on
AffectNet database. Each short spectrogram was considered as a picture and
processed by convolutional network too. As a base audio model we used ResNet
pretrained in speaker recognition task. Predictions from both modalities were
fused on decision level and improve single-channel approaches by a few percent
