Generative Listener EEG for Speech Emotion Recognition Using Generative Adversarial Networks with Compressed Sensing

Currently, emotional features in speech emotion recognition are typically extracted from the speeches, However, recognition accuracy can be influenced by factors such as semantics, language, and cross-speech datasets. Achieving consistent emotional judgment with human listeners is a key challenge for AI to address. Electroencephalography (EEG) signals prove to be an effective means of capturing authentic and meaningful emotional information in humans. This positions EEG as a promising tool for detecting emotional cues conveyed in speech. In this study, we proposed a novel approach named CS-GAN that generates listener EEGs in response to a speaker's speech, specifically aimed at enhancing cross-subject emotion recognition. We utilized generative adversarial networks (GANs) to establish a mapping relationship between speech and EEGs to generate stimulus-induced EEGs. Furthermore, we integrated compressive sensing theory (CS) into the GAN-based EEG generation method, thereby enhancing the fidelity and diversity of the generated EEGs. The generated EEGs were then processed using a CNN-LSTM model to identify the emotional categories conveyed in the speech. By averaging these EEGs, we obtained the event-related potentials (ERPs) to improve the cross-subject capability of the method. The experimental results demonstrate that the generated EEGs by this method outperform real listener EEGs by 9.31% in cross-subject emotion recognition tasks. Furthermore, the ERPs show an improvement of 43.59%, providing evidence for the effectiveness of this method in cross-subject emotion recognition.