TY - JOUR
T1 - Nomadic speech-based text entry
T2 - A decision model strategy for improved speech to text processing
AU - Price, Kathleen J.
AU - Lin, Min
AU - Feng, Jinjuan
AU - Goldman, Rich
AU - Sears, Andrew
AU - Jacko, Julie
N1 - Funding Information:
This material is based on work supported by the National Science Foundation (NSF) under Grant Nos. IIS-0121570 and IIS-0328391. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
PY - 2009/9
Y1 - 2009/9
N2 - Speech text entry can be problematic during ideal dictation conditions, but difficulties are magnified when external conditions deteriorate. Motion during speech is an extraordinary condition that might have detrimental effects on automatic speech recognition. This research examined speech text entry while mobile. Speech enrollment profiles were created by participants in both a seated and walking environment. Dictation tasks were also completed in both the seated and walking conditions. Although results from an earlier study suggested that completing the enrollment process under more challenging conditions may lead to improved recognition accuracy under both challenging and less challenging conditions, the current study provided contradictory results. A detailed review of error rates confirmed that some participants minimized errors by enrolling under more challenging conditions while others benefited by enrolling under less challenging conditions. Still others minimized errors when different enrollment models were used under the opposing condition. Leveraging these insights, we developed a decision model to minimize recognition error rates regardless of the conditions experienced while completing dictation tasks. When applying the model to existing data, error rates were reduced significantly but additional research is necessary to effectively validate the proposed solution.
AB - Speech text entry can be problematic during ideal dictation conditions, but difficulties are magnified when external conditions deteriorate. Motion during speech is an extraordinary condition that might have detrimental effects on automatic speech recognition. This research examined speech text entry while mobile. Speech enrollment profiles were created by participants in both a seated and walking environment. Dictation tasks were also completed in both the seated and walking conditions. Although results from an earlier study suggested that completing the enrollment process under more challenging conditions may lead to improved recognition accuracy under both challenging and less challenging conditions, the current study provided contradictory results. A detailed review of error rates confirmed that some participants minimized errors by enrolling under more challenging conditions while others benefited by enrolling under less challenging conditions. Still others minimized errors when different enrollment models were used under the opposing condition. Leveraging these insights, we developed a decision model to minimize recognition error rates regardless of the conditions experienced while completing dictation tasks. When applying the model to existing data, error rates were reduced significantly but additional research is necessary to effectively validate the proposed solution.
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U2 - 10.1080/10447310902964132
DO - 10.1080/10447310902964132
M3 - Article
AN - SCOPUS:77949382487
SN - 1044-7318
VL - 25
SP - 692
EP - 706
JO - International Journal of Human-Computer Interaction
JF - International Journal of Human-Computer Interaction
IS - 7
ER -