Speech emotion recognition (SER) systems have become essential in various fields,
including intelligent healthcare, customer service, call centers, automatic translation …

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 …

Emotion Prediction in Conversation (EPC) aims to forecast the emotions of forthcoming
utterances by utilizing preceding dialogues. Previous EPC approaches relied on simple …

Multimodal emotion recognition (MER), particularly using speech and text, is promising for
enhancing human-computer interaction. However, the efficacy of such systems is often …

Text data is commonly utilized as a primary input to enhance Speech Emotion Recognition
(SER) performance and reliability. However, the reliance on human-transcribed text in most …

In recent years, much research has been into speech emotion recognition (SER) using
multimodal data. Selective fusion of the features from different modalities is critical for …

The remarkable emergence of large language models (LLM) and their vast capabilities have
opened a possibility for applications in various fields, including speech emotion recognition …

Human emotion is usually expressed in multiple modalities, like audio and text. Multimodal
methods can boost Emotion Recognition. However, the relationship between audio and text …

Audio and visual data play vital roles in emotion recognition, with machine learning (ML)
methods like SVMs and deep neural networks excelling in inferring human emotions. This …

In recent years, speech emotion recognition (SER) methods that use both acoustic features
and text features derived through automatic speech recognition (ASR) have become …