10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Emotion Recognition Using a Hierarchical Binary Decision Tree Approach

Chi-Chun Lee, Emily Mower, Carlos Busso, Sungbok Lee, Shrikanth S. Narayanan

University of Southern California, USA

Emotion state tracking is an important aspect of human-computer 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 of binary 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.

Full Paper

Bibliographic reference.  Lee, Chi-Chun / Mower, Emily / Busso, Carlos / Lee, Sungbok / Narayanan, Shrikanth S. (2009): "Emotion recognition using a hierarchical binary decision tree approach", In INTERSPEECH-2009, 320-323.