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Behavior Computing
Emotion recognition using a hierarchical binary decision tree approach
Abstract
Emotion state tracking is an important aspect of humancomputer and human-robot interaction. It is important to design task specific emotion recognition systems for real-world applications. In this work, we propose a hierarchical structure loosely motivated by Appraisal Theory for emotion recognition. The levels in the hierarchical structure are carefully designed to place the easier classification task at the top level and delay the decision between highly ambiguous classes to the end. The proposed structure maps an input utterance into one of the five-emotion classes through subsequent layers ofbinary classifications. We obtain a balanced recall on each of the individual emotion classes using this hierarchical structure. The performance measure of the average unweighted recall percentage on the evaluation data set improves by 3.3% absolute (8.8% relative) over the baseline model.
Figures
Proposed Classification Framework: A Hierarchical Binary Decision Tree.
Proposed Classification Framework: A Hierarchical Binary Decision Tree.
Authors
Publication Date
2009/09/06
Conference
Interspeech
Interspeech 2009
DOI
10.21437/Interspeech.2009-105
Publisher
ISCA