Depression

Speech reflects underlying emotional, cognitive, and psychomotor states in depression and provides a non-invasive, real-time window into patient function. Redenlab’s digital assessments quantify these changes, enabling sensitive, repeatable endpoints for treatment monitoring and intervention trials.

Monitoring depression through speech

Depression affects millions globally and is a leading cause of disability, yet diagnosis and monitoring often rely on subjective reporting and infrequent clinical assessments. Changes in speech, such as reduced prosody, slowed tempo, and monotonic delivery, are well-documented markers of mood, psychomotor function, and cognitive load. These features reflect core functional impairments like energy, motivation, and executive functioning, yet are rarely measured objectively. Redenlab provides scalable, passive, and active speech analysis tools that capture subtle, clinically relevant vocal changes to support diagnosis, monitor treatment response, and deliver sensitive, real-world endpoints in mental health research and trials.

Speech analytics for Depression trials and care

Voice as a marker of mood and psychomotor state

Captures acoustic features linked to affect, mental effort, and processing speed, which are key dimensions of depressive symptomatology.

Scalable and low burden

Enables passive and active speech capture through mobile or telehealth platforms, ideal for decentralised or longitudinal designs.

Sensitive to treatment response

Tracks early and subtle changes in vocal tone, fluency, and articulation linked to medication or behavioural therapy effects.

Objective augmentation of clinical scales

Supplements subjective questionnaires (e.g., PHQ-9, MADRS) with continuous, real-world behavioural markers.

Regulatory-ready endpoints for digital mental health

Validated speech features align with functional domains relevant to both clinical care and digital therapeutic trials.

Redenlab’s experience in Depression research

Redenlab’s foundational work in depression began with one of the first large-scale investigations linking objective speech features to major depressive disorder (MDD), as published in Biological Psychiatry (Mundt et al., 2012). This study demonstrated that specific acoustic and prosodic features, such as slowed speech rate, reduced pitch variability, and increased pausing, correlated strongly with depression severity and could distinguish patients from healthy controls.

Redenlab’s team contributed to the development and validation of signal processing pipelines capable of extracting these markers in naturalistic speech samples. These early findings provided empirical support for the use of digital speech markers as objective, non-invasive indicators of psychomotor slowing, affective state, and cognitive load, which are domains central to functional impairment in depression.

This work supported Redenlab’s current suite of tools, which now enable longitudinal monitoring of depression-related speech changes across diverse clinical contexts, including remote care, decentralised trials, and digital therapeutics. Today, Redenlab continues to build on this evidence base, advancing voice analytics for clinical endpoint development, relapse prediction, and treatment response tracking in major depressive disorder and related mood conditions.