EmoSphere-SER：通过带辅助分类的球面表示增强语音情感识别
EmoSphere-SER: Enhancing Speech Emotion Recognition Through Spherical Representation with Auxiliary Classification
AI总结：
本文提出EmoSphere-SER模型，通过将VAD值映射为球面坐标并引入辅助区域分类任务引导回归，结合动态加权与多头自注意力池化，有效提升语音情感识别的准确性和一致性。
摘要：
语音情感识别通过离散标签或连续维度（如唤醒度、效价和支配度（VAD））从语音信号预测说话者的情感状态。我们提出了EmoSphere-SER，一个联合模型，该模型整合了球面VAD区域分类以引导VAD回归，从而改进情感预测。在我们的框架中，VAD值被转换为球面坐标，并划分为多个球面区域，一个辅助分类任务预测每个点所属的球面区域，从而引导回归过程。此外，我们引入了动态加权方案和带多头自注意力的风格池化层，以捕捉频谱和时间动态，进一步提升性能。这种联合训练策略强化了结构化学习并提高了预测一致性。实验结果表明，我们的方法超越了基线方法，验证了所提出框架的有效性。
英文摘要：
Speech emotion recognition predicts a speaker's emotional state from speech signals using discrete labels or continuous dimensions such as arousal, valence, and dominance (VAD). We propose EmoSphere-SER, a joint model that integrates spherical VAD region classification to guide VAD regression for improved emotion prediction. In our framework, VAD values are transformed into spherical coordinates that are divided into multiple spherical regions, and an auxiliary classification task predicts which spherical region each point belongs to, guiding the regression process. Additionally, we incorporate a dynamic weighting scheme and a style pooling layer with multi-head self-attention to capture spectral and temporal dynamics, further boosting performance. This combined training strategy reinforces structured learning and improves prediction consistency. Experimental results show that our approach exceeds baseline methods, confirming the validity of the proposed framework.
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。
