A 2D-approach towards the detection of distress using Fuzzy K-Nearest Neighbor
Date
2019-08
Authors
Machanje, Daniel
Orero, Joseph
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
This research focuses on a novel approach of distress detection referred to as the 2D
approach, using the fuzzy K-NN classification model. Unlike the traditional approach
where single emotions were qualified to depict distress such as fear, anxiety, or anger, the
2D approach introduces two phases of classification, with the first one checking the
speech excitement level, otherwise referred to as arousal in previous researches, and the
second one checking the speech polarity (negative or positive). Speech features are
obtained from the Berlin Database of Emotional Studies (BDES), and feature selection
done using the forward selection (FS) method. Attaining a distress detection accuracy of
86.64% using fuzzy K-NN, the proposed 2D approach shows promise in enhancing the
detection of emotional states having at least two emotions that could qualify the emotion
in question based on their original descriptions just as distress can be either one or many
of a number of emotions. Application areas for distress detection include health and
security for hostage scenario detection and faster medical response respectively
Description
Paper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 August 2019, Strathmore University, Nairobi, Kenya
Keywords
Speech, Emotions, Distress, 2D approach, Fuzzy K-NN