Poster: Predicting Opportune Moments for In-vehicle Proactive Speech Services

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Auditory-verbal or speech interactions with in-vehicle information systems have became increasingly popular. This opens up a whole new realm of possibilities for serving drivers with proactive speech services such as contextualized recommendations and interactive decision-making. However, prior studies have warned that such interactions may consume considerable attentional resources, thus degrade driving performance. This work aims to develop a machine learning model that can find opportune moments for the driver to engage in proactive speech interaction by using the vehicle and environment sensor data. Our machine learning analysis shows that opportune moments for interruption can be conservatively inferred with an accuracy of 0.74.
Publisher
ASSOC COMPUTING MACHINERY
Issue Date
2019-09
Language
English
Citation

ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) / ACM International Symposium on Wearable Computers (ISWC), pp.101 - 104

DOI
10.1145/3341162.3343841
URI
http://hdl.handle.net/10203/274971
Appears in Collection
IE-Conference Papers(학술회의논문)
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