Abstract:

In the field of online speech emotion recognition,the difficulty in training models often arises from the vast input data,making it challenging to capture meaningful emotional cues within the speech or meet high concurrent real-time demands due to complex network structures.To address this problem,a lightweight online speech emotion recognition method using self-supervised contrastive learning of CNN-GRU(Convolution Neural Network-Gated Recurrent Units) is proposed.By pretraining the model with the loss function of Contrastive Predictive Coding(CPC),it learns crucial emotional feature expressions from a lengthy conversational speech,addressing the issue of speech feature extraction.A lightweight encoder-decoder network structure of CNN-GRU is designed to meet real-time detection requirements.Raw audio is fed into a 1D-CNN to extract audio features,which are then passed through GRU units to obtain emotion category labels.Experimental comparisons conducted on thousands of recorded dialogues from a call center platform demonstrate that the proposed method achieves an accuracy of 96.97%.The results indicate the superiority of this approach in scenarios involving natural conversations and strong real-time constraints.

Key words: supervised learning, compare predictive coding, emotion recognition, CNN-GRU, lightweight structures, online identification