ISNet: Individual Standardization Network for Speech Emotion Recognition

Speech emotion recognition plays an essential role in human-computer interaction. However, cross-individual representation learning and individual-agnostic systems are challenging due to the distribution deviation caused by individual differences. The existing related approaches mostly use the auxiliary task of speaker recognition to eliminate individual differences. Unfortunately, although these methods can reduce interindividual voiceprint differences, it is difficult to dissociate interindividual expression differences since each individual has its unique expression habits. In this paper, we propose an individual standardization network (ISNet) for speech emotion recognition to alleviate the problem of interindividual emotion confusion caused by individual differences. Specifically, we model individual benchmarks as representations of nonemotional neutral speech, and ISNet realizes individual standardization using the automatically generated benchmark, which improves the robustness of individual-agnostic emotion representations. In response to individual differences, we also propose more comprehensive and meaningful individual-level evaluation metrics. In addition, we continue our previous work to construct a challenging large-scale speech emotion dataset (LSSED). We propose a more reasonable division method of the training set and testing set to prevent individual information leakage. Experimental results on datasets of both large and small scales have proven the effectiveness of ISNet, and the new state-of-the-art performance is achieved under the same experimental conditions on IEMOCAP and LSSED.