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

Automatic speech-based affect recognition of individuals in dyadic conversation is a challenging task, in part because of its heavy reliance on manual pre-processing. Traditional approaches frequently require hand-crafted speech features and segmentation of speaker turns. In this work, we design end-to-end deep learning methods to recognize each person's affective expression in an audio stream with two speakers, automatically discovering features and time regions relevant to the target speaker's affect. We integrate a local attention mechanism into the end-to-end architecture and compare the performance of three attention implementations -- one mean pooling and two weighted pooling methods. Our results show that the proposed weighted-pooling attention solutions are able to learn to focus on the regions containing target speaker's affective information and successfully extract the individual's valence and arousal intensity. Here we introduce and use a "dyadic affect in multimodal interaction - parent to child" (DAMI-P2C) dataset collected in a study of 34 families, where a parent and a child (3-7 years old) engage in reading storybooks together. In contrast to existing public datasets for affect recognition, each instance for both speakers in the DAMI-P2C dataset is annotated for the perceived affect by three labelers. To encourage more research on the challenging task of multi-speaker affect sensing, we make the annotated DAMI-P2C dataset publicly available, including acoustic features of the dyads' raw audios, affect annotations, and a diverse set of developmental, social, and demographic profiles of each dyad.Comment: Accepted by the 2020 International Conference on Multimodal Interaction (ICMI'20

DOCK8 is critical for the survival and function of NKT cells.

Patients with the dedicator of cytokinesis 8 (DOCK8) immunodeficiency syndrome suffer from recurrent viral and bacterial infections, hyper-immunoglobulin E levels, eczema, and greater susceptibility to cancer. Because natural killer T (NKT) cells have been implicated in these diseases, we asked if these cells were affected by DOCK8 deficiency. Using a mouse model, we found that DOCK8 deficiency resulted in impaired NKT cell development, principally affecting the formation and survival of long-lived, differentiated NKT cells. In the thymus, DOCK8-deficient mice lack a terminally differentiated subset of NK1.1(+) NKT cells expressing the integrin CD103, whereas in the liver, DOCK8-deficient NKT cells express reduced levels of the prosurvival factor B-cell lymphoma 2 and the integrin lymphocyte function-associated antigen 1. Although the initial NKT cell response to antigen is intact in the absence of DOCK8, their ongoing proliferative and cytokine responses are impaired. Importantly, a similar defect in NKT cell numbers was detected in DOCK8-deficient humans, highlighting the relevance of the mouse model. In conclusion, our data demonstrate that DOCK8 is required for the development and survival of mature NKT cells, consistent with the idea that DOCK8 mediates survival signals within a specialized niche. Accordingly, impaired NKT cell numbers and function are likely to contribute to the susceptibility of DOCK8-deficient patients to recurrent infections and malignant disease