MULTI-VIEW LEARNING IN DETECTION OF PSYCHOLOGICAL STATES

摘要

Systems and methods for using multi-view learning to leverage highly informative, high-cost, psychophysiological data collected in a laboratory setting, to improve post-traumatic stress disorder (PTSD) screening in the field, where only less-informative, low-cost, speech data are available. Partial least squares (PLS) methods can be used to learn a bilinear factor model, which projects speech and EEG responses onto latent spaces, where the covariance between the projections is maximized. The systems and methods use a speech representation based on a combination of audio and language descriptors extracted from spoken responses to open-ended questions.

主权项

1. A system for detecting psychological states, the system comprising:
a device to obtain sensor data corresponding to a subject's response to stimuli; a feature extraction processor to extract one or more features from the sensor data to generate feature representation data, the feature representation data being associated with a first view; a memory device configured to store a projection matrix modeling a relationship between the first view and a second view, and to store a detection model trained using training data associated with the first view transformed to a latent space using the projection matrix; and a detection module to transform the feature representation data to the latent space using the projection matrix, and to apply the transformed feature representation data to the detection model to detect one or more psychological states of the subject.