Speech Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/Pechckin/MountainCar
https://github.com/PeterJochem/Deep_RL
https://github.com/PeterJochem/Double_Deep_QLearning
https://github.com/Pezhin/best_yelp_review_project_ever
https://github.com/PhilippBach/hdm
https://github.com/PhilippBach/hdm_prev
https://github.com/PhilippeNguyen/keras_wavenet
https://github.com/Philori22/DDPG-aigym
https://github.com/PrideLee/sentiment-analysis
https://github.com/QEDan/links_clustering
https://github.com/QiaoLiuHit/LSOTB-TIR
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
https://github.com/Qsingle/imed_vision
SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image
https://github.com/Quiff789/Mapper-instability
A numerical measure of the instability of Mapper-type algorithms
https://github.com/Rami97rgb/French-to-English-Translator
https://github.com/RayeRen/RayeRen
PortaSpeech: Portable and High-Quality Generative Text-to-Speech
https://github.com/Remiphilius/PoemesProfonds
https://github.com/Remtasya/DDPG-Actor-Critic-Reinforcement-Learning-Reacher-Environment
https://github.com/RenXiangyuan/tf_bert
https://github.com/Rosie-Brigham/sesmeg
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/Ruby1302/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Ruby1302/DenseDepth_1
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Rudedaisy/attention-is-all-you-need-pytorch
https://github.com/SCismycat/bert_code_view
https://github.com/SER-2020-Project-ZX/Reference
https://github.com/SK-tklab/RandomFourierFeatures
Batched Large-scale Bayesian Optimization in High-dimensional Spaces
https://github.com/SSTGroup/Cyclostationary-Signal-Processing
https://github.com/SYangDong/bert-with-frozen-code
https://github.com/Salazar-99/Learning-State-Space-Models
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
https://github.com/Salazar-99/Recurrent-Kalman-Networks
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
https://github.com/SaminYeasar/off_policy_ac
https://github.com/Satan012/BERT
https://github.com/SejPal/SteganoGAN
https://github.com/SeniorDev009/ONNX-project
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/SeoroMin/transformer_pytorch
https://github.com/SeoroMin/transformer_pytorch_ver2
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.