MFHCA：通过多空间融合与分层协作注意力增强语音情感识别
MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative Attention
AI总结：
本文提出MFHCA方法，通过多空间融合模块和分层协作注意力模块结合频谱图与原始音频，在IEMOCAP数据集上使加权准确率和未加权准确率分别提升2.6%和1.87%，有效增强了语音情感识别。
摘要：
语音情感识别在人机交互中至关重要，但从音频中提取和使用情感线索面临挑战。本文提出MFHCA，一种在频谱图和原始音频上使用多空间融合与分层协作注意力进行语音情感识别的新方法。我们采用多空间融合模块（MF）高效识别与情感相关的频谱图区域，并整合Hubert特征以获取更高层声学信息。该方法还包含分层协作注意力模块（HCA），用于融合不同听觉层级的特征。我们在IEMOCAP数据集上评估该方法，在加权准确率和未加权准确率上分别实现了2.6%和1.87%的提升。大量实验证明了所提方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
