Abstract
Currently, AI-based assistive technologies, particularly those involving sensitive data, such as systems for detecting mental illness and emotional disorders, are full of confidentiality, integrity, and security compromises. In the aforesaid context, this work proposes an algorithm for detecting depressive states based on only three never utilized speech markers. This reduced number of markers offers a valuable protection of personal (sensitive) data by not allowing for the retrieval of the speaker’s identity. The proposed speech markers are derived from the analysis of pitch variations measured in speech data obtained through a tale reading task performed by typical and depressed subjects. A sample of 22 subjects (11 depressed and 11 healthy, according to both psychiatric diagnosis and BDI classification) were involved. The reading wave files were listened to and split into a sequence of intervals, each lasting two seconds. For each subject’s reading and each reading interval, the average pitch, the pitch variation (T), the average pitch variation (A), and the inversion percentage (also called the oscillation percentage O) were automatically computed. The values of the triplet (Ti, Ai, Oi) for the i-th subject provide, all together, a 100% correct discrimination between the speech produced by typical and depressed individuals, while requiring a very low computational cost and offering a valuable protection of personal data.
Highlights
The detection of depressive states is made through psychiatric examinations requiring the administration of psychiatric tests (such as the well-known Beck Depression Inventory (BDI) [1] test) and semi-structured interviews (such as Structured Clinical Interview-II (SCID-II) [2])
To protect citizens from these threats, the EU law on data protection and privacy in the European Union and European Economic Area (General Data Protection Regulation (GDPR)
According to the above discussion, it is clear that the present work is just a first step in a promising direction, and is a preliminary study toward an automatic procedure to discriminate between typical and depressed subjects through speech analysis while preserving their anonymity and privacy
Summary
The detection of depressive states is made through psychiatric examinations requiring the administration of psychiatric tests (such as the well-known Beck Depression Inventory (BDI) [1] test) and semi-structured interviews (such as Structured Clinical Interview-II (SCID-II) (https://www.appi.org/products/structured-clinical-interview-fordsm-5-scid-5, last accessed on 31 August 2021) [2]). These procedures are extremely timeconsuming, require a high level of expertise to reliably interpret the interviews’ outputs, and can be affected by clinicians’ theoretical orientations and an overestimation of patient’s progresses. Starting from the non-spontaneous tales of both clinically diagnosed depressive patients and non-depressed people while reading a pre-defined tale, this research concentrates on analysing nonverbal features
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