Phonologically Based Biomarkers for Major Depressive Disorder

Major Depressive Disorder (MDD) is a common mental health condition that can significantly impact a person's quality of life. Accurate and timely assessment of the condition is essential for effective diagnosis and treatment. Traditional assessment methods, such as the Hamilton-D (HAMD) scale, have faced criticism because of their subjectivity and potential limitations in reliability and validity. Current approaches to assessing MDD tend to rely on clinician interviews and self-reported questionnaires, which are subject to bias and may not provide the most accurate representation of a person's condition. Further, the lack of objective, automatic measures hinders the ability to detect and monitor depression consistently and accurately across different populations, leading to potential discrepancies in diagnosis and treatment.

Technology Description

This method and system involve using speech analysis to automatically assess conditions such as Major Depressive Disorder (MDD) in a subject. The process includes recognizing phones from the subject's speech and extracting prosodic or speech-excitation-source features. These features are used to generate an assessment of the condition on the basis of the correlation between the extracted features and the condition. The system consists of a phone recognizer, a feature extractor, and an assessment generator. The technology seeks to improve the validity and reliability of the standard Hamilton-D (HAMD) assessment for detecting depression. By providing an automatic means to detect and/or monitor depression, the invention aims to address the concerns associated with the subjectivity of HAMD assessments. This new method may reduce human error, making the process more efficient and potentially leading to more accurate results in detecting and monitoring depression.