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Automatic speech-based affect recognition of individuals in dyadicconversation is a challenging task, in part because of its heavy reliance onmanual pre-processing. Traditional approaches frequently require hand-craftedspeech features and segmentation of speaker turns. In this work, we designend-to-end deep learning methods to recognize each person's affectiveexpression in an audio stream with two speakers, automatically discoveringfeatures and time regions relevant to the target speaker's affect. We integratea local attention mechanism into the end-to-end architecture and compare theperformance of three attention implementations -- one mean pooling and twoweighted pooling methods. Our results show that the proposed weighted-poolingattention solutions are able to learn to focus on the regions containing targetspeaker's affective information and successfully extract the individual'svalence and arousal intensity. Here we introduce and use a "dyadic affect inmultimodal interaction - parent to child" (DAMI-P2C) dataset collected in astudy of 34 families, where a parent and a child (3-7 years old) engage inreading storybooks together. In contrast to existing public datasets for affectrecognition, each instance for both speakers in the DAMI-P2C dataset isannotated for the perceived affect by three labelers. To encourage moreresearch on the challenging task of multi-speaker affect sensing, we make theannotated DAMI-P2C dataset publicly available, including acoustic features ofthe dyads' raw audios, affect annotations, and a diverse set of developmental,social, and demographic profiles of each dyad.

Dyadic Speech-based Affect Recognition using DAMI-P2C Parent-child Multimodal Interaction Dataset