Modulation-based Speech Emotion Recognition with Reconstruction Error Feature Expansion

Speech emotion recognition is an ongoing research area, with applicability to fields such as human-machine interfaces, virtual assistance software, and forensics and law enforcement operations. Targeting the latter, a deep neural network (DNN) system is proposed for human affective state classification. The system uses modulation-based spectral features, together with the reconstruction error (RE) of the feature vector input. The RE is obtained using an autoencoder (AE), structurally identical to the DNN classifier. With this approach, improvements have been obtained over existing results on the Berlin Database of Emotional Speech (Emo-DB), reaching an unweighted accuracy (UA) of 87.3% for a reduced (5-class) set relevant for the targeted applications.