  Estimating personal well-being draws increasing attention particularly from
healthcare and pharmaceutical industries. We propose an approach to estimate
personal well-being in terms of various measurements such as anxiety, sleep
quality and mood using voice. With clinically validated questionnaires to score
those measurements in a self-assessed way, we extract salient features from
voice and train regression models with deep neural networks. Experiments with
the collected database of 219 subjects show promising results in predicting the
well-being related measurements; concordance correlation coefficients (CCC)
between self-assessed scores and predicted scores are 0.41 for anxiety, 0.44
for sleep quality and 0.38 for mood.
