Word-Level Emotion Recognition Using High-Level Features

In this paper, we investigate the use of high-level features for recognizing human emotions at the word-level in natural conversations with virtual agents. Experiments were carried out on the 2012 Audio/Visual Emotion Challenge (AVEC2012) database, where emotions are defined as vectors in the Arousal-Expectancy-Power-Valence emotional space. Our model using 6 novel disfluency features yields significant improvements compared to those using large number of low-level spectral and prosodic features, and the overall performance difference between it and the best model of the AVEC2012 Word-Level Sub-Challenge is not significant. Our visual model using the Active Shape Model visual features also yields significant improvements compared to models using the low-level Local Binary Patterns visual features. We built a bimodal model By combining our disfluency and visual feature sets and applying Correlation-based Feature-subset Selection. Considering overall performance on all emotion dimensions, our bimodal model outperforms the second best model of the challenge, and comes close to the best model. It also gives the best result when predicting Expectancy values.