Depression and anxiety disorders are highly prevalent worldwide. Attention to the adverse effects of depression on patient health, as well as its associated economic burden has been warranted. To support objective depression assessment, the affective computing community engaged signal processing, computer vision and machine learning approaches for analyzing verbal and non-verbal behavior of depressed patients and made predictions about what patterns should be indicative of depressed state. These studies have analyzed the relationship between objective measures of voice, speech, non-verbal behavior and clinical subjective ratings of severity of depression for the purpose of automatic depression assessment. Despite major advances have been achieved in recent years, there are still several open research directions to be solved in the study of depression: Audio and video features from individual only concern the paralinguistic information, such as speaking rate, facial action units (AUs), etc, rather than the linguistic information from the speaking con- tent, which can reflect the sleep status, emotional status, feeling and other life status of the individual. It is important to explore more effective audio, visual, linguistic and other multi-modal features, and design multi-modal fusion framework for depression recognition.
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DOI:
10.17148/IJARCCE.2022.11519
