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Traditional approaches to automatic emotion recognition are relying on theapplication of handcrafted features. More recently however the advent of deeplearning enabled algorithms to learn meaningful representations of input dataautomatically. In this paper, we investigate the applicability of transferringknowledge learned from large text and audio corpora to the task of automaticemotion recognition. To evaluate the practicability of our approach, we aretaking part in this year's Interspeech ComParE Elderly Emotion Sub-Challenge,where the goal is to classify spoken narratives of elderly people with respectto the emotion of the speaker. Our results show that the learned featurerepresentations can be effectively applied for classifying emotions from spokenlanguage. We found the performance of the features extracted from the audiosignal to be not as consistent as those that have been extracted from thetranscripts. While the acoustic features achieved best in class results on thedevelopment set, when compared to the baseline systems, their performancedropped considerably on the test set of the challenge. The features extractedfrom the text form, however, are showing promising results on both sets and areoutperforming the official baseline by 5.7 percentage points unweighted averagerecall.

Embedded Emotions -- A Data Driven Approach to Learn Transferable Feature Representations from Raw Speech Input for Emotion Recognition