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.

翻译：本研究探讨了语音识别技术在外围手术服务（Periop）中的应用，以使Periop工作人员能够利用移动技术记录工作流程里程碑。若此类语音识别技术能够稳定可靠，则可通过移动技术改善患者流程和护理质量。本实验旨在让Periop工作人员在无需被数据录入与查询任务打断的情况下提供护理服务。然而，研究结果可推广至工程管理人员试图通过移动技术提升通信性能的其他情境。本研究通过使用后处理分类器（即词袋模型、支持向量机和最大熵）增强了谷歌的语音识别能力。实验考察了三种因素（原始措辞、简化措辞与个性化措辞）在三个层级（零次训练重复、五次训练重复与十次训练重复）下的表现。结果表明，个性化措辞的正确率最高，且对设备进行个体语音训练同样能提升正确率。尽管方法简单，但词袋分类器显著提高了语音识别的正确率。研究发现最大熵与支持向量机算法的分类效率近乎一致。这些结果表明，工程管理人员可通过后处理技术显著增强谷歌语音识别技术，从而促进其在医疗保健及其他领域的应用。