Speech Emotion Recognition via Multi-Level Attention Network

Aiming to improve the performance of human speech emotion recognition (SER), the existing work has made great progress based on the popular mel-scale frequency cepstral coefficient (MFCC). However, the existing work rarely pays attention to the low-level emotion related features in MFCC, such as the underlying interactive relations. In this letter, we propose a novel multi-level attention network (MLAnet), which contains a multi-scale low-level feature (MLF) extractor and a multi-unit attention (MUA) module. Within the MLF extractor, we minimize the task-irrelevant information which harms the performance of SER by applying the attention mechanism. Since the features extracted by the MLF extractor contain rich domain-specific emotion information, we further present a MUA module to simultaneously weight the features in terms of time, frequency and channel dimensions. In this way, the discriminative emotion features in different dimensions can be extracted by corresponding weighting blocks. Experimental results on two benchmark datasets demonstrate that the proposed method outperforms other state-of-the-art approaches.

Automated summary

The aim of this research is to improve the performance of human speech emotion recognition. The proposed multi-level attention network (MLAnet) extracts low-level emotion features from the popular mel-scale frequency cepstral coefficient (MFCC) and weights these features using a multi-unit attention module. Experimental results show that this method outperforms other state-of-the-art approaches.