Analysis of Multi-Lingual Emotion Recognition Using Auditory Attention Features

In this paper, we build mono-lingual and cross-lingual emotion recognition systems and report performance on English and German databases. The emotion recognition system uses biologically inspired auditory attention features together with a neural network for learning the mapping between features and emotion classes. We first build mono-lingual systems for both Berlin Database of Emotional Speech (EMO-DB) and LDC’s Emotional Prosody (Emo-Prosody) and achieve 82.7% and 56.7% accuracy for five class emotion classification (neutral, sad, angry, happy, and boredom) using leave-one-speakerout cross validation. When tested with cross-lingual systems, the five-class emotion recognition accuracy drops to 55.1% and 41.4% accuracy for EMO-DB and Emo-Prosody, respectively. Finally, we build a bilingual emotion recognition system and report experimental results and their analysis. Bilingual system performs close to the performance of individual mono-lingual systems.