  Key challenges in developing generalized automatic emotion recognition
systems include scarcity of labeled data and lack of gold-standard references.
Even for the cues that are labeled as the same emotion category, the
variability of associated expressions can be high depending on the elicitation
context e.g., emotion elicited during improvised conversations vs. acted
sessions with predefined scripts. In this work, we regard the emotion
elicitation approach as domain knowledge, and explore domain transfer learning
techniques on emotional utterances collected under different emotion
elicitation approaches, particularly with limited labeled target samples. Our
emotion recognition model combines the gradient reversal technique with an
entropy loss function as well as the softlabel loss, and the experiment results
show that domain transfer learning methods can be employed to alleviate the
domain mismatch between different elicitation approaches. Our work provides new
insights into emotion data collection, particularly the impact of its
elicitation strategies, and the importance of domain adaptation in emotion
recognition aiming for generalized systems.
