Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation

Abstract

In Speech Emotion Recognition (SER), emotional characteristics often appear in diverse forms of energy patterns in spectrograms. Typical attention neural network classifiers of SER are usually optimized on a fixed attention granularity. In this paper, we apply multiscale area attention in a deep convolutional neural network to attend emotional characteristics with varied granularities and therefore the classifier can benefit from an ensemble of attentions with different scales. To deal with data sparsity, we conduct data augmentation with vocal tract length perturbation (VTLP) to improve the generalization capability of the classifier. Experiments are carried out on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset. We achieved 79.34% weighted accuracy (WA) and 77.54% unweighted accuracy (UA), which, to the best of our knowledge, is the state of the art on this dataset.

Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognition

“…AER has been extensively studied for audio (Xu et al 2021), text (Calefato, Lanubile, and Novielli 2017), facial clues (Chen et al 2016;Luo et al 2017), and EEG-based brain waves (Tripathi et al 2017). Previous studies showed Figure 1: Basic overview of our approach to multimodal emotion recognition.…”