Speakers - PAWC2025

Abstract

As the field of psychiatry evolves in the era of precision medicine and digital innovation, there is a growing demand for tools that are non-invasive, scalable, and capable of capturing mental states with greater objectivity and continuity. Among emerging approaches, voice analysis is increasingly recognized as a promising digital biomarker for psychiatric disorders, particularly for conditions such as depression, where subjective self-reporting and clinical rating scales often dominate diagnostic processes.

Human voice is deeply intertwined with affective, cognitive, and physiological processes. Subtle variations in prosody, pitch, speech rhythm, and articulation can reflect mood disturbances, psychomotor retardation, and cognitive dysfunctions. In recent studies, voice-derived features have demonstrated potential in detecting depressive symptoms, tracking clinical progress, and even differentiating diagnostic categories. These findings suggest that voice, an everyday and natural behavior, may offer a powerful window into mental health status—one that is cost-effective, remote-capable, and patient-friendly.

We find a series of findings from recent voice-based depression detection studies. These include analyses of temporal, spectral, and prosodic features from a diverse group of participants across age groups and clinical profiles. Preliminary results indicate that certain acoustic features are consistently associated with different dimension of depression, such as pause duration, pitch variability, and speech rate irregularities. These features may not only serve as markers for diagnosis but also provide insights into underlying psychopathological mechanisms.

Beyond individual features or models, we will emphasize a crucial next step in this line of research: the construction of voice-symptom association networks. Rather than treating voice features as isolated predictors, we propose mapping them onto specific clinical symptoms—such as anhedonia, psychomotor slowing, or cognitive impairment—based on structured assessments and clinical interviews. This network-based approach aims to bridge the gap between data-driven signal processing and clinically interpretable symptom domains, facilitating integration with existing diagnostic frameworks like the DSM or ICD.

Moreover, we will address the implications of voice analysis in real-world psychiatric practice, including its potential use in early screening or relapse monitoring. We will also reflect on ethical and methodological considerations, such as privacy, generalizability across languages and cultures, and the need for transparent, clinically validated models.

This work positions voice as an accessible, and objective biomarker with significant potential to augment traditional psychiatric diagnosis. By combining computational insights with clinical expertise, and by anchoring acoustic patterns to symptom-level interpretations, we envision a future where voice can help close the gap between subjective experience and measurable psychiatric insight.