Using Voice Technology to Identify Opioid Use in Patients in Treatment for Opioid Use Disorder

This study investigated whether changes in a person's voice could be used to identify opioid use in individuals with opioid use disorder (OUD). The opioid epidemic continues to present significant public health, medical, and social challenges in the United States and globally. Clinicians treating patients with OUD often need to determine whether a patient may be actively using opioids, intoxicated, withdrawing, or responding appropriately to treatment. Current approaches commonly rely on self-reporting, interviews, behavioral observations, urine toxicology testing, and clinical judgment. While these methods can be useful, they may also be subjective, resource-intensive, intermittent, invasive, or difficult to implement frequently in routine care settings.

The purpose of this project was to evaluate whether speech analysis technology could provide a more objective, scalable, and non-invasive approach for monitoring opioid-related physiological changes. Human speech is a complex neuromuscular activity that depends on the coordinated function of the brain, respiratory system, vocal tract, and facial musculature. Opioids can affect cognitive processing, respiratory patterns, motor coordination, reaction time, sedation levels, and muscle control, all of which may influence characteristics of speech production. Prior scientific literature has suggested that physiological and neurological conditions can sometimes produce measurable changes in speech patterns. This project sought to determine whether similar measurable changes could be associated with opioid use.

The study focused specifically on analyzing speech recordings from participants with opioid use disorder. Researchers collected voice samples and applied computational analysis methods to evaluate whether acoustic and temporal speech features could distinguish opioid-related states. The project used signal-processing techniques and machine learning methods to analyze a range of speech characteristics that may reflect physiological effects associated with opioid exposure.

Evaluated speech features included acoustic biomarkers commonly studied in speech analytics research. The project investigated whether combinations of these features could be used to identify patterns associated with opioid intoxication or withdrawal.

A major goal of the study was to assess the feasibility of using speech as a physiological biomarker for opioid use monitoring. Researchers evaluated whether machine learning models could reliably differentiate between opioid-related conditions using speech data alone.