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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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Greatest papers with code

Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation

3 Feb 2021makcedward/nlpaug

In this paper, we apply multiscale area attention in a deep convolutional neural network to attend emotional characteristics with varied granularities and therefore the classifier can benefit from an ensemble of attentions with different scales.

DATA AUGMENTATION MOTION CAPTURE SPEECH EMOTION RECOGNITION

COSMIC: COmmonSense knowledge for eMotion Identification in Conversations

6 Oct 2020declare-lab/conv-emotion

In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge.

EMOTION RECOGNITION IN CONVERSATION

Conversational Transfer Learning for Emotion Recognition

11 Oct 2019SenticNet/conv-emotion

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).

EMOTION RECOGNITION IN CONVERSATION TRANSFER LEARNING

DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation

IJCNLP 2019 SenticNet/conv-emotion

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.

EMOTION CLASSIFICATION EMOTION RECOGNITION IN CONVERSATION

Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

8 May 2019SenticNet/conv-emotion

Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI).

EMOTION RECOGNITION IN CONVERSATION

ExpNet: Landmark-Free, Deep, 3D Facial Expressions

2 Feb 2018fengju514/Expression-Net

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)

3D FACIAL EXPRESSION RECOGNITION EMOTION RECOGNITION FACIAL LANDMARK DETECTION