  Speech emotion recognition is crucial in human-computer interaction, but
extracting and using emotional cues from audio poses challenges. This paper
introduces MFHCA, a novel method for Speech Emotion Recognition using
Multi-Spatial Fusion and Hierarchical Cooperative Attention on spectrograms and
raw audio. We employ the Multi-Spatial Fusion module (MF) to efficiently
identify emotion-related spectrogram regions and integrate Hubert features for
higher-level acoustic information. Our approach also includes a Hierarchical
Cooperative Attention module (HCA) to merge features from various auditory
levels. We evaluate our method on the IEMOCAP dataset and achieve 2.6\% and
1.87\% improvements on the weighted accuracy and unweighted accuracy,
respectively. Extensive experiments demonstrate the effectiveness of the
proposed method.
