Predicting cognitive load levels from speech data
Citation:
Su Jing and Saturnino Luz, Predicting cognitive load levels from speech data, Proceedings of the International Conference on Non-Linear Speech Processing (NOLISP 2015), Vetri sul Mare, Italy, May 2015, Anna Esposito and Francesco Carlo Morabito, Springer, 2015, 1 - 8Download Item:
Abstract:
An analysis of acoustic features for a ternary cognitive load
classification task and an application of a classification boosting
method to the same task are presented. The analysis is based on a
data set that encompasses a rich array of acoustic features as well
as electroglottographic (EGG) data. Supervised and unsupervised
methods for identifying constitutive features of the data set are
investigated with the ultimate goal of improving prediction. Our
experiments show that the different tasks used to elicit the speech
for this challenge affect the acoustic features differently in terms
of their predictive power and that different feature selection
methods might be necessary across these sub-tasks. The sizes of the
training sets are also an important factor, as evidenced by the fact
that the use of boosting combined with feature selection was enough
to bring the unweighted recall scores for the Stroop tasks well
above a strong support vector machine baseline.
Author's Homepage:
http://people.tcd.ie/luzsDescription:
PUBLISHEDVetri sul Mare, Italy
Author: LUZ, SATURNINO
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Proceedings of the International Conference on Non-Linear Speech Processing (NOLISP 2015)Publisher:
SpringerType of material:
Conference PaperCollections
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Intelligent Content & CommunicationsMetadata
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