  Attention has become one of the most commonly used mechanisms in deep
learning approaches. The attention mechanism can help the system focus more on
the feature space's critical regions. For example, high amplitude regions can
play an important role for Speech Emotion Recognition (SER). In this paper, we
identify misalignments between the attention and the signal amplitude in the
existing multi-head self-attention. To improve the attention area, we propose
to use a Focus-Attention (FA) mechanism and a novel Calibration-Attention (CA)
mechanism in combination with the multi-head self-attention. Through the FA
mechanism, the network can detect the largest amplitude part in the segment. By
employing the CA mechanism, the network can modulate the information flow by
assigning different weights to each attention head and improve the utilization
of surrounding contexts. To evaluate the proposed method, experiments are
performed with the IEMOCAP and RAVDESS datasets. Experimental results show that
the proposed framework significantly outperforms the state-of-the-art
approaches on both datasets.
