JMIR Publications

ABSTRACT

Background:

While most research on automatic speech analysis (ASA) has focused on acoustic features, the potential of linguistic markers remains underexplored, particularly in clinically diagnosed, non-English-speaking populations.

Objective:

This study evaluated the integration of acoustic and linguistic markers for detecting depression in a Spanish-speaking clinical sample.

Methods:

The sample included 80 patients with major depressive disorder (MDD) or persistent depressive disorder (PDD) from the Psychiatry Department of Vall d'Hebron University Hospital and 71 healthy controls. Participants answered 11 open-ended questions related to depressive symptoms and well-being via a web-based platform. Linguistic and acoustic variables spanning four categories, namely, prosodic, cepstral, spectral, and Teager Energy Operator (TEO)-based features, were extracted. Group comparisons and logistic regressions were performed to assess the predictive value of acoustic and linguistic features. Machine learning models were used to compare the performance of acoustic, linguistic, and ensemble classification models combining both feature sets.

Results:

TEO-based and cepstral features showed the strongest predictive power. Among linguistic features, greater use of verbs, reduced use of nouns and past-tense verbs, smaller vocabulary size, and increased use of shorter words and sentences remained strong predictors of depression after adjusting for covariates. The linguistic model outperformed the acoustic model (AUC = 0.86 vs. = 0.79), while the ensemble model combining both acoustic and linguistic features achieved comparable overall performance (AUC = 0.86), with slightly improved accuracy (0.84) and specificity (0.93).

Conclusions:

Linguistic features differentiated patients with depression from healthy controls. With further validation and refinement, brief speech-based assessments could aid early depression detection in primary care and community settings.