  Emotion recognition has a pivotal role in affective computing and in
human-computer interaction. The current technological developments lead to
increased possibilities of collecting data about the emotional state of a
person. In general, human perception regarding the emotion transmitted by a
subject is based on vocal and visual information collected in the first seconds
of interaction with the subject. As a consequence, the integration of verbal
(i.e., speech) and non-verbal (i.e., image) information seems to be the
preferred choice in most of the current approaches towards emotion recognition.
In this paper, we propose a multimodal fusion technique for emotion recognition
based on combining audio-visual modalities from a temporal window with
different temporal offsets for each modality. We show that our proposed method
outperforms other methods from the literature and human accuracy rating. The
experiments are conducted over the open-access multimodal dataset CREMA-D.
