MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative Attention
<|reference_start|>MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative Attention: 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.<|reference_end|>
@article{jiao2024mfhca:,
    title={MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and
  Hierarchical Cooperative Attention},
    author={Xinxin Jiao, Liejun Wang, Yinfeng Yu},
    journal={arXiv preprint arXiv:2404.13509},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13509},
    primaryClass={cs.SD cs.AI eess.AS}
}