Domain generalization is a major challenge for cross-corpus speech emotion recognition.
The recognition performance built on" seen" source corpora is inevitably degraded when the …

Cross-corpus speech emotion recognition can be a useful transfer learning technique to
build a robust speech emotion recognition system by leveraging information from various …

Cross-corpus speech emotion recognition (SER) is a challenging task, and its difficulty lies
in the mismatch between the feature distributions of the training (source domain) and testing …

In this paper, we focus on the research of cross-corpus speech emotion recognition (SER),
in which the training (source) and testing (target) speech samples come from different …

Domain-invariant class-discriminative representation across multiple emotional speech
corpora is essential for multi-corpus speech emotion recognition. When training a model …

Speech Emotion Recognition (SER) has become a growing focus of research in human-
computer interaction. An essential challenge in SER is to extract common attributes from …

Speech emotion recognition helps enrich next-generation AI with emotional intelligence
abilities by grasping the emotion from voice and words. At the current stage, speech emotion …

Speech emotion recognition (SER) is important in enabling personalized services and
multimedia applications in our life. It also becomes a prevalent topic of research with its …

The application of cross‐corpus for speech emotion recognition (SER) via domain
adaptation methods have gain high acknowledgment for developing good robust emotion …

In this manuscript, the topic of multi-corpus Speech Emotion Recognition (SER) is
approached from a deep transfer learning perspective. A large corpus of emotional speech …