  The goal of Speech Emotion Recognition (SER) is to enable computers to
recognize the emotion category of a given utterance in the same way that humans
do. The accuracy of SER is strongly dependent on the validity of the
utterance-level representation obtained by the model. Nevertheless, the ``dark
knowledge" carried by non-target classes is always ignored by previous studies.
In this paper, we propose a hierarchical network, called DKDFMH, which employs
decoupled knowledge distillation in a deep convolutional neural network with a
fused multi-head attention mechanism. Our approach applies logit distillation
to obtain higher-level semantic features from different scales of attention
sets and delve into the knowledge carried by non-target classes, thus guiding
the model to focus more on the differences between sentiment features. To
validate the effectiveness of our model, we conducted experiments on the
Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset. We achieved
competitive performance, with 79.1% weighted accuracy (WA) and 77.1% unweighted
accuracy (UA). To the best of our knowledge, this is the first time since 2015
that logit distillation has been returned to state-of-the-art status.
