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speech-2

Emotion Recognition

3260 papers • 126 benchmarks • 313 datasets

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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Benchmarks

These leaderboards are used to track progress in emotion-recognition-8

Trend

Dataset

Best Model

Actions

RAVDESS

RAVDESS

Emomusic

Emomusic

MPED

MPED

Libraries

i

Use these libraries to find emotion-recognition-8 models and implementations

SenticNet/conv-emotion

5 papers 1,076

Datasets

Hateful Memes

Aff-Wild

SEED

Aff-Wild2

CARER

ISEAR

Subtasks

Speech Emotion Recognition Emotion Recognition in Conversation Multimodal Emotion Recognition Emotion-Cause Pair Extraction Emotion-Cause Pair Extraction

Most implemented papers

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

E. Cambria, Devamanyu Hazarika, Soujanya Poria, Navonil Majumder, Rada Mihalcea, Gautam Naik•Thu Oct 04 2018

The Multimodal EmotionLines Dataset (MELD), an extension and enhancement of Emotion lines, contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends and shows the importance of contextual and multimodal information for emotion recognition in conversations.

1404

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

SEED

SEED

MSP-Podcast

MSP-Podcast

HSE-asavchenko/face-emotion-recogni…

5 papers 257

tomas-gajarsky/facetorch

4 papers 117

aris-ai/Audio-and-text-based-emotio…

3 papers 78

ankurbhatia24/MULTIMODAL-EMOTION-RE…

3 papers 48

raulsteleac/Speech_Emotion_Recognit…

3 papers 14

priya22/emotiondynamics

2 papers 39

EmotionLines

EmotionLines

Hateful Memes Challenge

Hateful Memes Challenge

ExpW

EmoBank

Emotion Cause Extraction

Facial Emotion Recognition

EEG Emotion Recognition

Video Emotion Recognition

Emotion Recognition in Context

Speech Emotion Recognition in Russian

0

Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors

Paul Pu Liang, Amir Zadeh, Louis-philippe Morency, Yansen Wang, Ying Shen, Zhun Liu•Thu Nov 22 2018

The Recurrent Attended Variation Embedding Network (RAVEN) is proposed that models the fine-grained structure of nonverbal subword sequences and dynamically shifts word representations based on nonverbal cues to capture the dynamic nature of non verbal intents.

462 0

Multimodal Speech Emotion Recognition Using Audio and Text

Seunghyun Yoon, Seokhyun Byun, Kyomin Jung•Tue Oct 09 2018

The proposed model outperforms previous state-of-the-art methods in assigning data to one of four emotion categories when the model is applied to the IEMOCAP dataset, as reflected by accuracies ranging from 68.8% to 71.8%.

341 0

Multimodal Speech Emotion Recognition and Ambiguity Resolution

Gaurav Sahu•Thu Apr 11 2019

It is shown that lighter machine learning based models trained over a few hand-crafted features are able to achieve performance comparable to the current deep learning based state-of-the-art method for emotion recognition.

52 0

Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts

Rui Xia, Zixiang Ding•Mon Jun 03 2019

A 2-step approach is proposed to address this new ECPE task, which first performs individual emotion extraction and cause extraction via multi-task learning, and then conduct emotion-cause pairing and filtering.

234 0

DeXpression: Deep Convolutional Neural Network for Expression Recognition

A. Dengel, Muhammad Zeshan Afzal, M. Liwicki, Peter Burkert, Felix Trier•Wed Sep 16 2015

The proposed architecture is independent of any hand-crafted feature extraction and performs better than the earlier proposed convolutional neural network based approaches and visualize the automatically extracted features which have been learned by the network in order to provide a better understanding.

148 0

Multi-attention Recurrent Network for Human Communication Comprehension

Paul Pu Liang, Amir Zadeh, Louis-philippe Morency, E. Cambria, Soujanya Poria, Prateek Vij•Wed Jan 31 2018

The main strength of the model comes from discovering interactions between modalities through time using a neural component called the Multi-attention Block (MAB) and storing them in the hybrid memory of a recurrent part called the Long-short Term Hybrid Memory (LSTHM).

546 0

Efficient Low-rank Multimodal Fusion With Modality-Specific Factors

Paul Pu Liang, Amir Zadeh, Louis-philippe Morency, Ying Shen, Zhun Liu, V. Lakshminarasimhan•Mon Apr 30 2018

The Low-rank Multimodal Fusion method is proposed, which performs multimodal fusion using low-rank tensors to improve efficiency and is indeed much more efficient in both training and inference compared to other methods that utilize tensor representations.

852 0

Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis

Feiyang Chen, Ziqian Luo, Yanyan Xu•Tue Apr 16 2019

Surprisingly, DFF-ATMF also achieves new state-of-the-art results on the IEMOCAP dataset, indicating that the proposed fusion strategy also has a good generalization ability for multimodal emotion recognition.

86 0

Multitask Emotion Recognition with Incomplete Labels

Bertram E. Shi, Didan Deng, Zhaokang Chen•Sun Feb 09 2020

This work trains a unified model to perform three tasks: facial action unit detection, expression classification, and valence-arousal estimation, and proposes an algorithm for the multitask model to learn from missing (incomplete) labels.

99 0

Adding a benchmark result helps the community track progress.

Emotion Recognition | State-of-the-Art