Learning to detect vocal hyperfunction from ambulatory neck-surface acceleration features: Initial results for vocal fold nodules

摘要

Voice disorders are medical conditions that often result from vocal abuse/misuse which is referred to generically as vocal hyperfunction. Standard voice assessment approaches cannot accurately determine the actual nature, prevalence, and pathological impact of hyperfunctional vocal behaviors because such behaviors can vary greatly across the course of an individual’s typical day and may not be clearly demonstrated during a brief clinical encounter. Thus, it would be clinically valuable to develop non-invasive ambulatory measures that can reliably differentiate vocal hyperfunction from normal patterns of vocal behavior.As an initial step towards this goal we used an accelerometer taped to the neck surface to provide a continuous, non-invasive acceleration signal designed to capture some aspects of vocal behavior related to a common manifestation of vocal hyperfunction; vocal cord nodules. We gathered data from 12 female adult patients diagnosed with vocal fold nodules and 12 control speakers matched for age and occupation. We derived features from weeklong neck-surface acceleration recordings by using distributions of sound pressure level and fundamental frequency over five-minute windows of the acceleration signal and normalized these features so that inter-subject comparisons were meaningful. We then used supervised machine learning to show that the two groups exhibit distinct vocal behaviors that can be detected using the acceleration signal.We were able to correctly classify 22 of the 24 subjects, suggesting that in the future measures of the acceleration signal could be used to detect patients with the types of aberrant vocal behaviors that are associated with hyperfunctional voice disorders.