Multimodal emotion recognition (MER) refers to the identification and understanding of
human emotional states by combining different signals, including—but not limited to—text …

Meticulous learning of human emotions through speech is an indispensable function of
modern speech emotion recognition (SER) models. Consequently, deriving and interpreting …

Unsupervised cross-corpus speech emotion recognition (SER) is the task where the labeled
training (source) and unlabeled testing (target) speech come from different corpora …

Deep learning has been widely used in multi-modal Speech Emotion Recognition (SER) to
learn sentiment-related features by aggregating representations from multiple modes …

Multimodal emotion recognition (MER) integrates multiple modalities to identify the user's
emotional state, which is the core technology of natural and friendly human–computer …

The impressive development of facial manipulation techniques has raised severe public
concerns. Identity-aware methods, especially suitable for protecting celebrities, are seen as …

The multimodal emotion recognition has attracted more attention in recent decades. Though
remarkable progress has been achieved with the rapid development of deep learning …

This work investigates the synchronization issue of fractional-order inertial neural networks
(FOINNs) by employing a cumulative reduced-order method. Firstly, a novel cumulative …

Long-term memory often plays a pivotal role in human cognition through the analysis of
contextual information. Machine learning researchers have attempted to emulate this …

Speech contains rich emotional information, especially in its time and frequency domains.
Therefore, extracting emotional information from these domains to model the global …