The advancements in neural networks and the on-demand need for accurate and near real-
time Speech Emotion Recognition (SER) in human–computer interactions make it …

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 …

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

In this work, we explore the dependencies between speaker recognition and emotion
recognition. We first show that knowledge learned for speaker recognition can be reused for …

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 …

When training data are scarce, it is challenging to train a deep neural network without
causing the overfitting problem. For overcoming this challenge, this article proposes a new …

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 …

Generative adversarial networks (GANs) have seen remarkable progress in recent years.
They are used as generative models for all kinds of data such as text, images, audio, music …

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 …

Audio-visual emotion recognition is the research of identifying human emotional states by
combining the audio modality and the visual modality simultaneously, which plays an …