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Beyond acoustic features: Incorporating linguistic variables in automatic speech analysis for depression detection
Patricia Laura Maran1, Peru Gabirondo2, Alexandra Vlaic3
1Psychiatry, Mental Health and Addictions Group, Vall d'Hebron Research Institute (VHIR), Instituto de Investigación Sanitaria Acreditado Instituto de Investigación - Hospital Universitario Vall d'Hebron (IR-HUVH), Barcelona, Catalonia, Spain; Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain.
Linguistic markers in speech show promise for detecting depression in Spanish speakers. Combining acoustic and linguistic features improved accuracy in this clinical study.
Area of Science:
- Speech analysis
- Computational linguistics
- Clinical psychology
Background:
- Automatic speech analysis (ASA) predominantly uses acoustic features, neglecting linguistic markers.
- Linguistic markers are underexplored in non-English speaking, clinically diagnosed populations.
Purpose of the Study:
- To evaluate the integration of acoustic and linguistic markers for depression detection.
- To assess the predictive value of these markers in a Spanish-speaking clinical sample.
Main Methods:
- 151 participants (80 with MDD/PDD, 71 controls) provided speech via open-ended questions.
- Extracted linguistic and acoustic features (prosodic, cepstral, spectral, Teager Energy Operator).
- Employed logistic regression and machine learning models for classification.
Main Results:
- Linguistic features (e.g., verb/noun usage, vocabulary size) were strong depression predictors.
- The linguistic model (AUC=0.86) outperformed the acoustic model (AUC=0.79).
- Ensemble model achieved comparable performance (AUC=0.86) with high accuracy (0.84) and specificity (0.93).
Conclusions:
- Integrating linguistic features into ASA enhances depression detection.
- Speech-based assessments hold potential for early depression screening in primary care settings.
