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Dementia is a family of neurogenerative conditions affecting memory and cognition in an increasing number of individuals in our globally aging population. Automated analysis of language, speech and paralinguistic indicators have been gaining popularity as potential indicators of cognitive decline. Here we propose a novel longitudinal multi-modal dataset collected from people with mild dementia and age matched controls over a period of several months in a natural setting. The multi-modal data consists of spoken conversations, a subset of which are transcribed, as well as typed and written thoughts and associated extra-linguistic information such as pen strokes and keystrokes. We describe the dataset in detail and proceed to focus on a task using the speech modality. The latter involves distinguishing controls from people with dementia by exploiting the longitudinal nature of the data. Our experiments showed significant differences in how the speech varied from session to session in the control and dementia groups.
Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS), especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently large and comprehensive datasets is currently a bottleneck for t
Incorporating multi-modal contexts in conversation is an important step for developing more engaging dialogue systems. In this work, we explore this direction by introducing MMChat: a large scale multi-modal dialogue corpus (32.4M raw dialogues and 1
We present a new publicly available dataset with the goal of advancing multi-modality learning by offering vision and language data within the same context. This is achieved by obtaining data from a social media website with posts containing multiple
Medical dialogue systems (MDSs) aim to assist doctors and patients with a range of professional medical services, i.e., diagnosis, consultation, and treatment. However, one-stop MDS is still unexplored because: (1) no dataset has so large-scale dialo
Recent work in open-domain conversational agents has demonstrated that significant improvements in model engagingness and humanness metrics can be achieved via massive scaling in both pre-training data and model size (Adiwardana et al., 2020; Roller