Evaluation of Google's Voice Recognition and Sentence Classification for
  Health Care Applications
Majbah Uddin Nathan Huynh Jose M Vidal Kevin M Taaffe Lawrence D Fredendall Joel S Greenstein
This study examined the use of voice recognition technology in perioperative
services (Periop) to enable Periop staff to record workflow milestones using
mobile technology. The use of mobile technology to improve patient flow and
quality of care could be facilitated if such voice recognition technology could
be made robust. The goal of this experiment was to allow the Periop staff to
provide care without being interrupted with data entry and querying tasks.
However, the results are generalizable to other situations where an engineering
manager attempts to improve communication performance using mobile technology.
This study enhanced Google's voice recognition capability by using
post-processing classifiers (i.e., bag-of-sentences, support vector machine,
and maximum entropy). The experiments investigated three factors (original
phrasing, reduced phrasing, and personalized phrasing) at three levels (zero
training repetition, 5 training repetitions, and 10 training repetitions).
Results indicated that personal phrasing yielded the highest correctness and
that training the device to recognize an individual's voice improved
correctness as well. Although simplistic, the bag-of-sentences classifier
significantly improved voice recognition correctness. The classification
efficiency of the maximum entropy and support vector machine algorithms was
found to be nearly identical. These results suggest that engineering managers
could significantly enhance Google's voice recognition technology by using
post-processing techniques, which would facilitate its use in health care and
other applications.