LF-GANet: Local Frame-Level Global Dynamic Attention Network for Speech Emotion Recognition

Speech emotion recognition (SER) is an important field of human–computer interaction. Although humans have various ways of expressing emotions, speech is one of the most direct ways. Therefore, it is an important technical challenge to extract the emotional information from the speech signal as much as possible. To address this issue, we proposed a local frame-level global dynamic attention network (LF-GANet) to extract emotional information from speech signals. This network mainly consists of two parts, a local frame-level module (LFM) and a global dynamic attention module (GAM). To extract rich frame-level emotional information from speech signals, the LFM was designed to extract features from forward and reverse time series separately; the GAM real-time extracted the global correlations from speech signals. We conducted experiments on the EMODB and SAVEE datasets. The results showed that our method outperformes the existing SOTA model in UAR on both datasets, verifying the effectiveness of the model.