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 …

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 letter, we propose a novel deep domain-adaptive convolutional neural network
(DDACNN) model to handle the challenging cross-corpus speech emotion recognition …

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 …

In this paper, we propose a novel deep transfer learning method called deep implicit
distribution alignment networks (DIDAN) to deal with cross-corpus speech emotion …

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 …

In this paper, we focus on the research of cross-corpus speech emotion recognition (SER),
in which the training and testing speech signals in cross-corpus SER belong to dierent …

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 has become an attractive research topic due to various
emotional states of speech signals in real-life scenarios. Most current speech emotion …

Abstract Speech Emotion Recognition (SER) performance degrades when their training and
test conditions or corpora differ. Cross-corpus SER (CCSER) is a research branch that …