Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …

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 …

Speech self-supervised models such as wav2vec 2.0 and HuBERT are making revolutionary
progress in Automatic Speech Recognition (ASR). However, they have not been totally …

Speech Emotion Recognition (SER) aims to help the machine to understand human's
subjective emotion from only audio in-formation. However, extracting and utilizing …

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

Speech emotion recognition (SER) classifies speech into emotion categories such as:
Happy, Angry, Sad and Neutral. Recently, deep learning has been applied to the SER task …

Artificial intelligence (AI) and machine learning (ML) are employed to make systems smarter.
Today, the speech emotion recognition (SER) system evaluates the emotional state of the …

Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs),
such that the prediction of the infected model will be maliciously changed if the hidden …

Speech Emotion Recognition (SER) refers to the use of machines to recognize the emotions
of a speaker from his (or her) speech. SER benefits Human-Computer Interaction (HCI). But …

Artificial intelligence, deep learning, and machine learning are dominant sources to use in
order to make a system smarter. Nowadays, the smart speech emotion recognition (SER) …