Reusing Neural Speech Representations for Auditory Emotion Recognition
This work addresses feature selection challenges in emotion recognition for speech processing applications, but it is incremental as it builds on existing transfer learning methods.
The paper tackled the problem of identifying relevant and robust features for acoustic emotion recognition by reusing neural speech representations from large speech databases, achieving ~10% relative improvements in accuracy and F1-score over a baseline recurrent neural network on the IEMOCAP dataset.
Acoustic emotion recognition aims to categorize the affective state of the speaker and is still a difficult task for machine learning models. The difficulties come from the scarcity of training data, general subjectivity in emotion perception resulting in low annotator agreement, and the uncertainty about which features are the most relevant and robust ones for classification. In this paper, we will tackle the latter problem. Inspired by the recent success of transfer learning methods we propose a set of architectures which utilize neural representations inferred by training on large speech databases for the acoustic emotion recognition task. Our experiments on the IEMOCAP dataset show ~10% relative improvements in the accuracy and F1-score over the baseline recurrent neural network which is trained end-to-end for emotion recognition.