  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.
