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Acoustic emotion recognition aims to categorize the affective state of thespeaker and is still a difficult task for machine learning models. Thedifficulties come from the scarcity of training data, general subjectivity inemotion perception resulting in low annotator agreement, and the uncertaintyabout which features are the most relevant and robust ones for classification.In this paper, we will tackle the latter problem. Inspired by the recentsuccess of transfer learning methods we propose a set of architectures whichutilize neural representations inferred by training on large speech databasesfor the acoustic emotion recognition task. Our experiments on the IEMOCAPdataset show ~10% relative improvements in the accuracy and F1-score over thebaseline recurrent neural network which is trained end-to-end for emotionrecognition.

Reusing Neural Speech Representations for Auditory Emotion Recognition