  The impression we make on others depends not only on what we say, but also,
to a large extent, on how we say it. As a sub-branch of affective computing and
social signal processing, impression recognition has proven critical in both
human-human conversations and spoken dialogue systems. However, most research
has studied impressions only from the signals expressed by the emitter,
ignoring the response from the receiver. In this paper, we perform impression
recognition using a proposed cross-domain architecture on the dyadic IMPRESSION
dataset. This improved architecture makes use of cross-domain attention and
regularization. The cross-domain attention consists of intra- and
inter-attention mechanisms, which capture intra- and inter-domain relatedness,
respectively. The cross-domain regularization includes knowledge distillation
and similarity enhancement losses, which strengthen the feature connections
between the emitter and receiver. The experimental evaluation verified the
effectiveness of our approach. Our approach achieved a concordance correlation
coefficient of 0.770 in competence dimension and 0.748 in warmth dimension.
