The substantial growth of Internet-of-Things technology and the ubiquity of smartphone
devices has increased the public and industry focus on speech emotion recognition (SER) …

Most state-of-the-art speech systems use deep neural networks (DNNs). These systems
require a large amount of data to be learned. Hence, training state-of-the-art frameworks on …

Emotion and sentiment play a central role in various human activities, such as perception,
decision-making, social interaction, and logical reasoning. Develo** artificial emotional …

Human body actions are an important form of non-verbal communication in social
interactions. This paper focuses on a specific subset of body actions known as micro …

The paper proposes an integrated speech emotion conversion framework developed using
speaker-independent mixed-lingual training. The key contribution of the work is non-parallel …

In real-world scenarios, new audio classes with insufficient samples usually emerge
continually, which motivates the study of few-shot class-incremental audio classification …

Speech emotion recognition (SER) is not only a ubiquitous aspect of everyday
communication, but also a central focus in the field of human–computer interaction …

Whilst deep learning techniques have achieved excellent emotion prediction, they still
require large amounts of labelled training data, which are (a) onerous and tedious to …

Most speech emotion recognition methods based on frames have obtained good results in
many applications. However, they segment each speech sample into smaller frames that are …

This paper presents MOCAS, a multimodal dataset dedicated for human cognitive workload
(CWL) assessment. In contrast to existing datasets based on virtual game stimuli, the data in …