Speech Emotion Recognition with Local-Global Aware Deep Representation Learning

Abstract

Convolutional neural network (CNN) based deep representation learning methods for speech emotion recognition (SER) have demonstrated great success. The basic design of CNN restricts the ability to model only local information well. Capsule network (CapsNet) can overcome the shortages of CNNs to capture the shallow global features from the spectrogram, although CapsNet cannot learn the local and deep global information. In this paper, we propose a local-global aware deep representation learning system that mainly includes two modules. One module contains a multi-scale CNN, time- frequency CNN (TFCNN) to learn the local representation. In the other module, we introduce a structure with dense connections of multiple blocks to learn shallow and deep global information. Every block in this structure is a complete CapsNet improved by a new routing algorithm. The local and global representations are fed to the classifier and achieve an absolute increase of at least 4.25% than benchmarks on IEMOCAP.