Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition
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In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge.
Ranked #1 on Emotion Recognition in Conversation on EmoryNLP
We propose an approach, TL-ERC, where we pre-train a hierarchical dialogue model on multi-turn conversations (source) and then transfer its parameters to a conversational emotion classifier (target).
Ranked #2 on Emotion Recognition in Conversation on DailyDialog
Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources.
Ranked #1 on Emotion Recognition in Conversation on SEMAINE
Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI).
Ranked #5 on Emotion Recognition in Conversation on EC
Emotion recognition in conversations is crucial for building empathetic machines.
Ranked #3 on Emotion Recognition in Conversation on SEMAINE
Emotion recognition in conversations is crucial for the development of empathetic machines.
Ranked #5 on Emotion Recognition in Conversation on SEMAINE
Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.
Ranked #1 on 3D Facial Expression Recognition on 2017_test set (using extra training data)
Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication.
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.
Ranked #8 on Emotion Recognition in Conversation on MELD