Uncovering Voice Misuse Using Symbolic Mismatch
This work addresses voice disorders affecting millions, offering an objective diagnostic tool, but it is incremental as it applies an unsupervised data mining approach to a new domain.
The study tackled the problem of diagnosing behavioral voice disorders by analyzing long-term ambulatory accelerometer data from 22 subjects, showing significant differences between patients and controls and some pre- and post-treatment patients.
Voice disorders affect an estimated 14 million working-aged Americans, and many more worldwide. We present the first large scale study of vocal misuse based on long-term ambulatory data collected by an accelerometer placed on the neck. We investigate an unsupervised data mining approach to uncovering latent information about voice misuse. We segment signals from over 253 days of data from 22 subjects into over a hundred million single glottal pulses (closures of the vocal folds), cluster segments into symbols, and use symbolic mismatch to uncover differences between patients and matched controls, and between patients pre- and post-treatment. Our results show significant behavioral differences between patients and controls, as well as between some pre- and post-treatment patients. Our proposed approach provides an objective basis for helping diagnose behavioral voice disorders, and is a first step towards a more data-driven understanding of the impact of voice therapy.