Speech as a promising biosignal in precision psychiatry

Area of Science:

• Digital Health
• Biomedical Engineering
• Psychiatry

Background:

• Current healthcare relies on infrequent assessments, missing opportunities for early health event detection and prevention.
• New health technologies enable continuous, non-invasive monitoring of health using speech analysis via smartphones.
• Speech analysis can extract health-relevant biosignals linked to biological pathways, showing promise for detecting disorders like depression and schizophrenia.

Purpose of the Study:

• To explore the potential of speech analysis for continuous health monitoring.
• To identify key speech signals for detecting psychological stress and its impact on mental and physical health.
• To discuss the validation and translation of speech-based data into clinical biomarkers and interventions.

Main Methods:

• Utilizing smartphone technology to analyze voice and speech for health-relevant biosignals.
• Extracting a wide variety of biosignals from speech patterns.
• Linking speech-derived biosignals to mental and physical health outcomes.

Main Results:

• Speech analysis can detect various health-relevant biosignals.
• These biosignals show potential in identifying disorders such as depression and schizophrenia.
• Assessing psychological stress through speech can monitor its impact on outcomes like self-harm, substance abuse, and disease recurrence.

Conclusions:

• Speech is a novel digital biosignal with the potential for high-frequency, scalable health assessments.
• Further research is needed to identify critical speech signals, validate them, and translate them into clinical tools.
• Secure speech analysis can play a crucial role in predicting clinical outcomes and delivering timely, tailored interventions.