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A continuous-speech interface to a decision support system: II. An evaluation using a Wizard-of-Oz experimental
W M Detmer1, S Shiffman, J C Wyatt
1Section on Medical Informatics, Stanford University School of Medicine, CA 94305-5479.
Journal of the American Medical Informatics Association : JAMIA
|January 1, 1995
Summary
A continuous-speech interface for decision support systems achieved 81% semantic accuracy, even with speech recognition errors below 50%. Language processing techniques improved performance, demonstrating the utility of the Wizard-of-Oz design for evaluating speech input systems.
Area of Science:
- Medical Informatics
- Human-Computer Interaction
- Speech Recognition Technology
Background:
- Continuous-speech interfaces offer potential for efficient interaction with medical decision support systems.
- Evaluating the accuracy of these interfaces is crucial for their clinical adoption.
- Previous methods may not fully capture real-world performance nuances.
Purpose of the Study:
- To evaluate the performance of a continuous-speech interface linked to a medical decision support system.
- To assess both speech recognition accuracy and the semantic accuracy of term matching.
- To explore the effectiveness of a Wizard-of-Oz experimental design in this evaluation.
Main Methods:
- A prospective evaluation using a Wizard-of-Oz experimental design.
- Physicians spoke utterances describing clinical findings into a speech interface.
- Utterances were matched against controlled-vocabulary terms from Quick Medical Reference (QMR).
Main Results:
- Overall speech recognition accuracy was below 50%.
- Semantic accuracy, using keyword matching language-processing techniques, reached 81%.
- The Wizard-of-Oz design facilitated a robust evaluation.
Conclusions:
- Language processing techniques can significantly improve semantic accuracy despite speech misrecognition.
- The Wizard-of-Oz experimental design is a valuable method for evaluating speech-input systems.
- This approach holds promise for future development and assessment of voice-enabled medical technologies.
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