Speech technology is not appropriately explored even though modern advances in speech
technology—especially those driven by deep learning (DL) technology—offer …

Affective computing is one of the most important research fields in modern human–computer
interaction (HCI). The goal of affective computing is to study and develop the theories …

Traditionally, speech emotion recognition (SER) research has relied on manually
handcrafted acoustic features using feature engineering. However, the design of …

In recent years, deep learning (DL) based intelligent fault diagnosis method has been widely
applied in the field of equipment fault diagnosis. However, most of the existing methods are …

Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence
(AI) by endowing autonomous systems with high levels of understanding of the real world …

Despite the recent advancement in speech emotion recognition (SER) within a single corpus
setting, the performance of these SER systems degrades significantly for cross-corpus and …

Research on speech processing has traditionally considered the task of designing hand-
engineered acoustic features (feature engineering) as a separate distinct problem from the …

Despite the recent advances in multi-task learning of dense prediction problems, most
methods rely on expensive labelled datasets. In this paper, we present a label efficient …

This paper focuses on the active interpretability for deep learning-based speech emotion
recognition (SER). To achieve this, we propose an explicit feature constrained model, the …

Multi-modal conversation emotion recognition (MCER) aims to recognize and track the
speaker's emotional state using text, speech, and visual information in the conversation …