30,884 research outputs found
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Research has proven that stress reduces quality of life and causes many
diseases. For this reason, several researchers devised stress detection systems
based on physiological parameters. However, these systems require that
obtrusive sensors are continuously carried by the user. In our paper, we
propose an alternative approach providing evidence that daily stress can be
reliably recognized based on behavioral metrics, derived from the user's mobile
phone activity and from additional indicators, such as the weather conditions
(data pertaining to transitory properties of the environment) and the
personality traits (data concerning permanent dispositions of individuals). Our
multifactorial statistical model, which is person-independent, obtains the
accuracy score of 72.28% for a 2-class daily stress recognition problem. The
model is efficient to implement for most of multimedia applications due to
highly reduced low-dimensional feature space (32d). Moreover, we identify and
discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US
Evaluating the Usability of Automatically Generated Captions for People who are Deaf or Hard of Hearing
The accuracy of Automated Speech Recognition (ASR) technology has improved,
but it is still imperfect in many settings. Researchers who evaluate ASR
performance often focus on improving the Word Error Rate (WER) metric, but WER
has been found to have little correlation with human-subject performance on
many applications. We propose a new captioning-focused evaluation metric that
better predicts the impact of ASR recognition errors on the usability of
automatically generated captions for people who are Deaf or Hard of Hearing
(DHH). Through a user study with 30 DHH users, we compared our new metric with
the traditional WER metric on a caption usability evaluation task. In a
side-by-side comparison of pairs of ASR text output (with identical WER), the
texts preferred by our new metric were preferred by DHH participants. Further,
our metric had significantly higher correlation with DHH participants'
subjective scores on the usability of a caption, as compared to the correlation
between WER metric and participant subjective scores. This new metric could be
used to select ASR systems for captioning applications, and it may be a better
metric for ASR researchers to consider when optimizing ASR systems.Comment: 10 pages, 8 figures, published in ACM SIGACCESS Conference on
Computers and Accessibility (ASSETS '17
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