Abstract
We investigate the role of top-down task drive attention
in the cocktail party problem. In a recently proposed computational
model of top-down attention it is possible to simulate the cocktail party
problem and make predictions about sensitivity to confounders under
different levels of attention. Based on such simulations we expect that
under strong top-down attention pattern recognition is improved as the
model can compensate for noise and confounders. We next investigate
the role of temporal and spectral overlaps and speech intelligibility in
humans, and how the presence of a task influences their relation. For this
purpose, we perform behavioral experiments inspired by Cherry’s classic
experiments carried out almost sixty years ago. We make participants
listen to a mono signal consisting of two different narratives pronounced
by a speech synthesizer under two different conditions. In the first case,
participants listen with no specific task, while in the second one they are
asked to follow one of the stories. Participants report the words they heard
by choosing from a list which also includes terms not present in any of
the narratives. We define temporal and spectral overlaps using the ideal
binary mask (IBMs) as a gauge. We analyze the correlation between
overlaps and the amount of reported words. We observe a significant
negative correlation when there is no task, while no correlation is detected
when a task is involved. Hence, results that are well aligned with the
simulation results in our computational top-down attention model.
Original language | English |
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Title of host publication | Proceedings of the tenth International Conference on Machine Learning and Applications (ICMLA'11) |
Publisher | IEEE |
Publication date | 2011 |
Publication status | Published - 2011 |
Event | 10th International Conference on Machine Learning and Applications (ICMLA 2011) - Honolulu, Hawaii, United States Duration: 18 Dec 2011 → 21 Dec 2011 Conference number: 10 |
Conference
Conference | 10th International Conference on Machine Learning and Applications (ICMLA 2011) |
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Number | 10 |
Country/Territory | United States |
City | Honolulu, Hawaii |
Period | 18/12/2011 → 21/12/2011 |