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

During the last decade, Speech Emotion Recognition (SER) has emerged as an integral
component within Human-computer Interaction (HCI) and other high-end speech processing …

Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

Similar to humans' cognitive ability to generalize knowledge and skills, self-supervised
learning (SSL) targets discovering general representations from large-scale data. This …

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

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 …

Precise recognition of emotion from speech signals aids in enhancing human–computer
interaction (HCI). The performance of a speech emotion recognition (SER) system depends …

Speech emotion recognition (SER) plays a significant role in human–machine interaction.
Emotion recognition from speech and its precise classification is a challenging task because …

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 …

Conversational emotion recognition is a crucial research topic in human-computer
interactions. Due to the heavy annotation cost and inevitable label ambiguity, collecting …