Investigating the Variability of Voice Quality and Pain Levels as a Function of Multiple Clinical Parameters

Abstract

Pain is an internal construct with vocal manifestation that varies as a function of personal and clinical attributes. Understanding the vocal indicators of pain-levels is important in providing an objective analytic in clinical assessment and intervention. In this work, we focus on investigating the variability of voice quality as a function of multiple clinical parameters at different pain-levels, specifically for emergency room patients during triage. Their pain-induced pathological voice quality characteristics are naturally affected by an individual attributes such as age, gender and pain-sites. We conduct a detailed multivariate statistical analysis on a 181 unique patient's vocal quality using recordings of real triage sessions. Our analysis show several important insights, 1) voice quality only varies statistically with pain-levels when interacting effect from other clinical parameters is considered, 2) senior group shows a higher value of voicing probability and shimmer when experiencing severe pain, 3) patients with abdomen pain have a lower jitter and shimmer during severe pain that is different from patients experiencing musculoskeletal pathology, and 4) there could be a relationship between the variation in the voice quality and the neural pathway of pain as evident by interacting with the pain-site factor.

Voice Patterns for Classification and Regression of Atherosclerotic Coronary Disease

“…Speech analysis has been widely used with success for the detection and classi cation of several health conditions and pathological states: Alzheimer disease [8], amyotrophic lateral sclerosis [9], autism [10], COVID-19 [11], depression [12], pain intensity [13], Parkinson's [14], pulmonary hypertension [15], schizophrenia [16], snoring [17], and others. When compared with other methods, speech analysis is a low-cost non-invasive approach whose usage can be transparent to the individual (for example to detect Parkinson's disease using a mobile phone [18]).…”

A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence

“…In light of this, observational pain scales are advised for use among adults in critical conditions when self-reported pain measurements are not feasible. Even so, the reliability and validity of these instruments are still limited, because even qualified raters cannot ensure an unbiased judgment [ 5 , 14 , 15 , 16 , 17 , 18 , 19 ].…”