  Traditional approaches to automatic emotion recognition are relying on the
application of handcrafted features. More recently however the advent of deep
learning enabled algorithms to learn meaningful representations of input data
automatically. In this paper, we investigate the applicability of transferring
knowledge learned from large text and audio corpora to the task of automatic
emotion recognition. To evaluate the practicability of our approach, we are
taking part in this year's Interspeech ComParE Elderly Emotion Sub-Challenge,
where the goal is to classify spoken narratives of elderly people with respect
to the emotion of the speaker. Our results show that the learned feature
representations can be effectively applied for classifying emotions from spoken
language. We found the performance of the features extracted from the audio
signal to be not as consistent as those that have been extracted from the
transcripts. While the acoustic features achieved best in class results on the
development set, when compared to the baseline systems, their performance
dropped considerably on the test set of the challenge. The features extracted
from the text form, however, are showing promising results on both sets and are
outperforming the official baseline by 5.7 percentage points unweighted average
recall.

Audio and Speech Processing;Machine Learning;Sound