Automatic and manual extraction of speech and language characteristics associated with depression

Abstract

Early and accurate diagnosis and intervention of depression is important to facilitate timely, direct, and appropriate interventions with potential for improved clinical outcomes. Delays in the diagnosis of patients with depression may be reduced if simple tools were available to indicate probability of diagnosis. Clinicians use speech and language characteristics to establish current mental state and diagnosis. The use of automatic acoustic feature extraction allows leveraging pitch, power, and variability and can provide an unbiased evaluation of speech.
 This study examined speech samples from a youth at-risk cohort, aged 9-25, and developed a manual rating system of speech and language characteristics, which involved rating short segments of audio and transcript on emotion, sentiment, affect, and richness. This competed against an automated model of extracting zero-crossing rate, energy, the entropy of energy, and Mel-frequency cepstral coefficients to identify speech characteristics associated with major depressive disorder.
 The results showed that the automatic feature extraction outcompeted the manual rating system in explaining the difference in speech between participants with and without major depressive disorder through speech and language characteristics. While the extraction of audio features is not a substitute for the clinical interview, the ability to provide an unbiased prediction of vulnerability to depression from speech may assist clinicians in early diagnosis.