As a sub-branch of affective computing, impression recognition, e.g., perception of speaker characteristics such as warmth or competence, is potentially a critical part of both human-human conversations and spoken dialogue systems. Most research has studied impressions only from the behaviors expressed by the speaker or the response from the listener, yet ignored their latent connection. In this paper, we perform impression recognition using a proposed listener adaptive cross-domain architecture, which consists of a listener adaptation function to model the causality between speaker and listener behaviors and a cross-domain fusion function to strengthen their connection. The experimental evaluation on the dyadic IMPRESSION dataset verified the efficacy of our method, producing concordance correlation coefficients of 78.8% and 77.5% in the competence and warmth dimensions, outperforming previous studies. The proposed method is expected to be generalized to similar dyadic interaction scenarios.

翻译：作为情感计算的一个分支，印象识别（例如对说话者温暖度或能力等特征的感知）在人机对话及口语对话系统中均可能扮演关键角色。现有研究多从说话者表达的行为或听者的反应中单独分析印象，而忽视了两者之间的潜在关联。本文提出一种基于听者自适应跨域架构的印象识别方法，该架构包含一个听者自适应函数以建模说话者与听者行为间的因果关系，以及一个跨域融合函数以强化二者关联。在二元IMPRESSION数据集上的实验验证了本方法的有效性，在能力维度和温暖度维度上分别取得了78.8%和77.5%的一致性相关系数，优于以往研究。所提方法可推广至类似的二元交互场景。