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

Emotion recognition has recently attracted extensive interest due to its significant
applications to human–computer interaction. The expression of human emotion depends on …

New research into human-computer interaction seeks to consider the consumer's emotional
status to provide a seamless human-computer interface. This would make it possible for …

Speech emotion recognition (SER) is traditionally performed using merely acoustic
information. Acoustic features, commonly are extracted per frame, are mapped into emotion …

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 …

The expression of emotions in human communication plays a very important role in the
information that needs to be conveyed to the partner. The forms of expression of human …

In Speech Emotion Recognition (SER), emotional characteristics often appear in diverse
forms of energy patterns in spectrograms. Typical attention neural network classifiers of SER …

Sentiment and emotion, which correspond to long-term and short-lived human feelings, are
closely linked to each other, leading to the fact that sentiment analysis and emotion …

Spoken question answering (SQA) requires fine-grained understanding of both spoken
documents and questions for the optimal answer prediction. In this paper, we propose novel …

The task of multi-modal emotion recognition in conversation (MERC) aims to analyze the
genuine emotional state of each utterance based on the multi-modal information in the …