Optimizing ambiguous speech emotion recognition through spatial–temporal parallel network with label correction strategy

Speech emotion recognition is of great significance for improving the human–computer interaction experience. However, traditional methods based on hard labels have difficulty dealing with the ambiguity of emotional expression. Existing studies alleviate this problem by redefining labels, but still rely on the subjective emotional expression of annotators and fail to consider the truly ambiguous speech samples without dominant labels fully. To solve the problems of insufficient expression of emotional labels and ignoring ambiguous undominantly labeled speech samples, we propose a label correction strategy that uses a model with exact sample knowledge to modify inappropriate labels for ambiguous speech samples, integrating model training with emotion cognition, and considering the ambiguity without dominant label samples. It is implemented on a spatial–temporal parallel network, which adopts a temporal pyramid pooling (TPP) to process the variable-length features of speech to improve the recognition efficiency of speech emotion. Through experiments, it has been shown that ambiguous speech after label correction has a more promoting effect on the recognition performance of speech emotions.