Abstract

Automated emotion state tracking is a crucial element in the computational study of human communication behaviors. It is important to design robust and reliable emotion recognition systems that are suitable for real-world applications both to enhance analytical abilities to support human decision making and to design human–machine interfaces that facilitate efficient communication. We introduce a hierarchical computational structure to recognize emotions. The proposed structure maps an input speech utterance into one of the multiple emotion classes through subsequent layers of binary classifications. The key idea is that the levels in the tree are designed to solve the easiest classification tasks first, allowing us to mitigate error propagation. We evaluated the classification framework on two different emotional databases using acoustic features, the AIBO database and the USC IEMOCAP database. In the case of …

Date

2011

Authors

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

Journal

Speech communication

Volume

53

Issue

9-10

Pages

1162-1171

Publisher

North-Holland