Two-level discriminative speech emotion recognition model with wave field dynamics: A personalized speech emotion recognition method

Presently available speech emotion recognition (SER) methods generally rely on a single SER model. Getting a higher accuracy of SER involves feature extraction method and model design scheme in the speech. However, the generalization performance of models is typically poor because the emotional features of different speakers can vary substantially. The present work addresses this issue by applying a two-level discriminative model to the SER task. The first level places an individual speaker within a specific speaker group according to the speaker's characteristics. The second level constructs a personalized SER model for each group of speakers using the wave field dynamics model and a dual-channel general SER model. Two-level discriminative model are fused for implementing an ensemble learning scheme to achieve effective SER classification. The proposed method is demonstrated to provide higher SER accuracy in experiments based on interactive emotional dynamic motion capture (IEMOCAP) corpus and a custom-built SER corpus. In IEMOCAP corpus, the proposed model improves the recognition accuracy by 7%. In custom-built SER corpus, both masked and unmasked speakers is employed to demonstrate that the proposed method maintains higher SER accuracy.