Speech is the most natural way of expressing ourselves as humans. It is only natural then to
extend this communication medium to computer applications. We define speech emotion …

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 …

In the past decade, convolutional neural networks (CNNs) have been widely adopted as the
main building block for end-to-end audio classification models, which aim to learn a direct …

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 …

Recognizing human emotions by machines is a complex task. Deep learning models
attempt to automate this process by rendering machines to exhibit learning capabilities …

As an important branch of affective computing, Speech Emotion Recognition (SER) plays a
vital role in human–computer interaction. In order to mine the relevance of signals in audios …

While speech emotion recognition (SER) has been an active research field since the last
three decades, the techniques that deal with the natural environment have only emerged in …

Speech emotion recognition is a challenging problem because human convey emotions in
subtle and complex ways. For emotion recognition on human speech, one can either extract …

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 …