72 research outputs found
Drawing-Based Automatic Dementia Screening Using Gaussian Process Markov Chains
Screening tests play an important role for early detection of dementia. Among those widely used screening tests, drawing tests have gained much attention in clinical psychology. Traditional evaluation of drawing tests totally relies on the appearance of drawn picture, but does not consider any time-dependent behaviour. We demonstrated that the processing speed and direction can reflect the decline of cognitive function, and thus may be useful for disease screening. We proposed a model of Gaussian process Markov chains (GPMC) to study the complex associations within the drawing data. Specifically, we modeled the process of drawing in a state-space form, where a drawing state is composed of drawing direction and velocity with consideration of the processing time. For temporal modeling, our scope focused more on discrete-time Markov chains on continuous state space. Because of the short processing time of picture drawing, we applied higher-order of Markov chains to model long-term temporal correlation across drawing states. Gaussian process regression was used for universal function approximation to flexibly infer the state transition function. With Gaussian process prior to the distribution of function space, we could encode high-level function properties such as noisiness, smoothness and periodicity. We also derived an efficient training mechanism for complex Gaussian process regression on bivariate Markov chains. With GPMC, we present an optimal decision rule based on Bayesian decision theory. We applied our proposed method to a drawing test for dementia screening, i.e. interlocking pentagon-drawing test. We tested our models with 256 subjects who are aged from 65 to 95. Finally, comparing to the traditional methods, our models showed remarkable improvement in drawing test for dementia screening
A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition
As a common way of emotion signaling via non-linguistic vocalizations, vocal
burst (VB) plays an important role in daily social interaction. Understanding
and modeling human vocal bursts are indispensable for developing robust and
general artificial intelligence. Exploring computational approaches for
understanding vocal bursts is attracting increasing research attention. In this
work, we propose a hierarchical framework, based on chain regression models,
for affective recognition from VBs, that explicitly considers multiple
relationships: (i) between emotional states and diverse cultures; (ii) between
low-dimensional (arousal & valence) and high-dimensional (10 emotion classes)
emotion spaces; and (iii) between various emotion classes within the
high-dimensional space. To address the challenge of data sparsity, we also use
self-supervised learning (SSL) representations with layer-wise and temporal
aggregation modules. The proposed systems participated in the ACII Affective
Vocal Burst (A-VB) Challenge 2022 and ranked first in the "TWO'' and "CULTURE''
tasks. Experimental results based on the ACII Challenge 2022 dataset
demonstrate the superior performance of the proposed system and the
effectiveness of considering multiple relationships using hierarchical
regression chain models.Comment: 5 pages, 3 figures, 5 table
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