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Speech Emotion Recognition

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하위 태스크: 4
평가표: 17
보존된 평가 기록: 48

Speech Emotion Recognition is a task of speech processing and computational paralinguistics that aims to recognize and categorize the emotions expressed in spoken language. The goal is to determine the emotional state of a speaker, such as happiness, anger, sadness, or frustration, from their speech patterns, such as prosody, pitch, and rhythm.

For multimodal emotion recognition, please upload your result to Multimodal Emotion Recognition on IEMOCAP

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/tanjeffreyz/attention-is-all-you-need

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https://github.com/taotaonice/FaceShifter

FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping

https://github.com/tatp22/linformer-pytorch

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https://github.com/tatp22/multidim-positional-encoding

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https://github.com/tbmoon/LANL_Earthquake_Prediction

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https://github.com/tcnguyen/bert

Attention Is All You Need

https://github.com/tcxdgit/MachineTranslation_t2t

Attention Is All You Need

https://github.com/tdmeeste/SparseSeqModels

Breaking the Softmax Bottleneck: A High-Rank RNN Language Model

https://github.com/tegg89/DLCamp_Jeju2018

Continuous control with deep reinforcement learning

https://github.com/tegg89/magnet

Continuous control with deep reinforcement learning

https://github.com/tegg89/magnn

Continuous control with deep reinforcement learning

https://github.com/tegg89/mann

Continuous control with deep reinforcement learning

https://github.com/tejaswigowda/semseg-pytorch

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/tensorlayer/RLzoo

Continuous control with deep reinforcement learning

https://github.com/tensorops/TransformerX

Attention Is All You Need

https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN

Cascade R-CNN: Delving into High Quality Object Detection

https://github.com/text-machine-lab/transformerpy

Attention Is All You Need

https://github.com/text-to-audio/make-an-audio

ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech

https://github.com/tgisaturday/dalle-lightning

Generating Diverse High-Fidelity Images with VQ-VAE-2

https://github.com/tgisaturday/dalle-lightning-tpu

Generating Diverse High-Fidelity Images with VQ-VAE-2

https://github.com/tgisaturday/taming-transformers-tpu

Generating Diverse High-Fidelity Images with VQ-VAE-2

https://github.com/thainv0212/re-ddpg

Continuous control with deep reinforcement learning

https://github.com/thanhlong1997/bert_quora

Attention Is All You Need

https://github.com/the-Quert/iNLPfun

Attention Is All You Need

https://github.com/theQuert/inlpfun

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https://github.com/thecodemasterk/BERT

Attention Is All You Need

https://github.com/theziqi/dccc

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

https://github.com/thiagorjes/depth_estimation

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/thomasslloyd/FitSpatial

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/thoughtmachines/Human-Pose-Estimation-using-HRNets

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/thuml/Transfer-Learning-Library

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

https://github.com/thumt/THUMT

Attention Is All You Need

https://github.com/thunlp-mt/ckd

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https://github.com/tian-kun/Fly-LSH

Improving Similarity Search with High-dimensional Locality-sensitive Hashing

https://github.com/tianxili/GNC

High-dimensional Gaussian graphical model for network-linked data

https://github.com/tiennhamhd/steganoganhd

SteganoGAN: High Capacity Image Steganography with GANs

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