A Novel Framework for Distress Detection through an Automated Speech Processing System

Based on our ongoing work, this work in progress project aims to develop an automated system to detect distress in people to enable early referral for interventions to target anxiety and depression, to mitigate suicidal ideation and to improve adherence to treatment. The project will utilize either use existing voice data to assess people into various scales of distress, or will collect voice data as per existing standards of distress measurement, to develop basic computing algorithms required to detect various attributes associated with distress, detected through a person's voice in a telephone call to a helpline. This will be then matched with the already available psychological assessment instruments such as the Distress Thermometer for these persons. In order to trigger interventions, organizational contexts are essential as interventions rely on the type of distress. Therefore, the model will be tested on various organizational settings such as the Police, Emergency and Health along with the Distress detection instruments normally used in a psychological assessment for accuracy and validation. The outcome of the project will culminate in a fully automated integrated system, and will save significant resources to organizations. The translation of the project will be realized in step-change improvements to quality of life within the gamut of public policy.