  Voice disorders affect a large portion of the population, especially heavy
voice users such as teachers or call-center workers. Most voice disorders can
be treated effectively with behavioral voice therapy, which teaches patients to
replace problematic, habituated voice production mechanics with optimal voice
production technique(s), yielding improved voice quality. However, treatment
often fails because patients have difficulty differentiating their habitual
voice from the target technique independently, when clinician feedback is
unavailable between therapy sessions. Therefore, with the long term aim to
extend clinician feedback to extra-clinical settings, we built two systems that
automatically differentiate various voice qualities produced by the same
individual. We hypothesized that 1) a system based on i-vectors could classify
these qualities as if they represent different speakers and 2) such a system
would outperform one based on traditional voice signal processing algorithms.
Training recordings were provided by thirteen amateur actors, each producing 5
perceptually different voice qualities in connected speech: normal, breathy,
fry, twang, and hyponasal. As hypothesized, the i-vector system outperformed
the acoustic measure system in classification accuracy (i.e. 97.5\% compared to
77.2\%, respectively). Findings are expected because the i-vector system maps
features to an integrated space which better represents each voice quality than
the 22-feature space of the baseline system. Therefore, an i-vector based
system has potential for clinical application in voice therapy and voice
training.
