Combining Ranking and Classification to Improve Emotion Recognition in Spontaneous Speech

We introduce a novel emotion recognition approach which integrates ranking models. The approach is speaker independent, yet it is designed to exploit information from utterances from the same speaker in the test set before making predictions. It achieves much higher precision in identifying emotional utterances than a conventional SVM classifier. Furthermore we test several possibilities for combining conventional classification and predictions based on ranking. All combinations improve overall prediction accuracy. All experiments are performed on the FAU AIBO database which contains realistic spontaneous emotional speech. Our best combination system achieves 6.6% absolute improvement over the Interspeech 2009 emotion challenge baseline system on the 5-class classification tasks.