Despite significant progress, speech emotion recognition (SER) remains challenging due to inherent complexity and ambiguity of the emotion attribute, particularly in wild world. Whereas current studies primarily focus on recognition and generalization capabilities, this work pioneers an exploration into the reliability of SER methods and investigates how to model the speech emotion from the aspect of data distribution across various speech attributes. Specifically, we first build a novel CNN-based SER model which adopts additive margin softmax loss to expand the distance between features of different classes, thereby enhancing their discrimination. Second, a novel multiple speech attribute control method MSAC is proposed to explicitly control speech attributes, enabling the model to be less affected by emotion-agnostic attributes and capture more fine-grained emotion-related features. Third, we make a first attempt to test and analyze the reliability of the proposed SER workflow using the out-of-distribution detection method. Extensive experiments on both single and cross-corpus SER scenarios show that our proposed unified SER workflow consistently outperforms the baseline in terms of recognition, generalization, and reliability performance. Besides, in single-corpus SER, the proposed SER workflow achieves superior recognition results with a WAR of 72.97\% and a UAR of 71.76\% on the IEMOCAP corpus.

通过建立一种新型的基于卷积神经网络的语音情感识别模型，以及提出一种新颖的多种语音属性控制方法 MSAC，该研究通过对数据分布的建模来探索语音情感的可靠性，并首次尝试使用超出分布检测方法测试和分析提出的情感识别工作流程的可靠性，实验证明了该方法在单一或跨语料库情感识别中具有优越的性能。

MSAC: 多音频情绪识别的多语音属性控制方法