Obtaining large, human labelled speech datasets to train models for emotion recognition is a
notoriously challenging task, hindered by annotation cost and label ambiguity. In this work …

Inspite the emerging importance of Speech Emotion Recognition (SER), the state-of-the-art
accuracy is quite low and needs improvement to make commercial applications of SER …

In Audio-Video Emotion Recognition (AVER), the idea is to have a human-level
understanding of emotions from video clips. There is a need to bring these two modalities …

In this paper, we show that convolutional neural networks can be directly applied to temporal
low-level acoustic features to identify emotionally salient regions without the need for …

Computational representation of everyday emotional states is a challenging task and,
arguably, one of the most fundamental for affective computing. Standard practice in emotion …

Many paralinguistic tasks are closely related and thus representations learned in one
domain can be leveraged for another. In this paper, we investigate how knowledge can be …

A critical issue of current speech-based sequence-to-one learning tasks, such as speech
emotion recognition (SER), is the dynamic temporal modeling for speech sentences with …

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems
lack generalisation across different conditions. A key underlying reason for poor …

Despite recent advancements in speech emotion recognition (SER) models, state-of-the-art
deep learning (DL) approaches face the challenge of the limited availability of annotated …

Exploiting the multimodal and temporal interaction between audio-visual channels is
essential for automatic audio-video emotion recognition (AVER). Modalities' strength in …