Multimodal Acoustic-Linguistic Machine Learning for Postpartum Depression Screening Using the Edinburgh Postnatal Depression Scale.

BACKGROUND

Postpartum depression (PPD) is a mood disorder affecting women during pregnancy or after childbirth, characterized by persistent sadness, anxiety, and difficulty bonding with the newborn. Symptoms range from mild emotional disturbances to severe depressive episodes requiring urgent clinical intervention. Despite the availability of treatments, PPD often remains underdiagnosed and undertreated. This study demonstrates an efficient method to predict the Edinburgh Postnatal Depression Scale (EPDS) scores based on combined acoustic and linguistic features extracted from speech.

METHODS

Pregnant and postpartum women aged 18 years or older were recruited for a structured screening study conducted between 2023 and 2024 (n = 275). Each participant completed the full EPDS questionnaire and provided a single open-ended audio response recorded via a smartphone application. A multimodal approach combining linguistic and acoustic features was used to predict EPDS scores, capturing both semantic content and paralinguistic markers, such as vocal pitch, speech rate, and prosody, reflective of mood and affect. Features from both modalities were combined using a regressor to produce a numeric EPDS score ranging from 0 to 30.

RESULTS

The model achieved a sensitivity of 84.4% (95% confidence interval [CI]: 77.5-89.5) and a specificity of 76.9% (95% CI: 69.0-82.2) at the ≥10 screening threshold, with an area under the curve of 0.886 (95% CI: 0.845-0.922). Regression performance demonstrated a Pearson correlation of R = 0.749 (95% CI: 0.69-0.80) and a mean absolute error of 3.42 compared with the ground-truth EPDS scores.

CONCLUSION

This model demonstrates strong screening performance for identifying PPD risk from brief video responses and can augment clinical workflows by prioritizing follow-up evaluation when elevated EPDS risk is detected.