Emotions are an integral part of human interactions and are significant factors in determining
user satisfaction or customer opinion. speech emotion recognition (SER) modules also play …

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

Recent years have seen important advances in the quality of state-of-the-art models, but this
has come at the expense of models becoming less interpretable. This survey presents an …

Automatic human affect recognition is a key step towards more natural human-computer
interaction. Recent trends include recognition in the wild using a fusion of audiovisual and …

Speech emotion recognition is a challenging task, and extensive reliance has been placed
on models that use audio features in building well-performing classifiers. In this paper, we …

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

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 …

In this work, we address the problem of data imbalance for the task of Speech Emotion
Recognition (SER). We investigate conditioned data augmentation using Generative …

This paper proposes an attention pooling based representation learning method for speech
emotion recognition (SER). The emotional representation is learned in an end-to-end …

Speech emotion recognition is a challenging task for three main reasons: 1) human emotion
is abstract, which means it is hard to distinguish; 2) in general, human emotion can only be …