Papers with Code 아카이브
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/veikkahonkanen/Seizurenet-replica
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
https://github.com/victorpham1997/Local_ICNet
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/viet-hoang-99/Lite_HRnet_vh
https://github.com/vijay120/bert
https://github.com/vikashranjan/transformer
https://github.com/vinairesearch/phogpt
https://github.com/vinayprabhu/X-is-all-you-need
https://github.com/vincent-thevenin/Realistic-Neural-Talking-Head-Models
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/vinthony/s2crnet-demos
Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization
https://github.com/vishnuanil122/Bert-Binary-Text-Classifier
https://github.com/visionNoob/hrnet_pytorch
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/vpj/jax_transformer
https://github.com/vpsg-research/waveguard
SimSwap: An Efficient Framework For High Fidelity Face Swapping
https://github.com/vulus98/rethinking-attention
https://github.com/vvvm23/TchAIkovsky-Legacy
https://github.com/vvvm23/vqvae-2
https://github.com/w-sugar/prtr
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/walkacross/transformer-pytorch
https://github.com/wangguangyuan/ClusterContrast
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/wangmz15/Chinese-Error-Correction-with-THUMT
https://github.com/wangyuan249/Mymmt767
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/wangyx240/High-Resolution-Image-Inpainting
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting
https://github.com/wangyx240/High-Resolution-Image-Inpainting-GAN
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting
https://github.com/warnikchow/coaudiotext
Speech Emotion Recognition Using Multi-hop Attention Mechanism
https://github.com/wassname/rl-portfolio-management
https://github.com/wayalhruhi/gogle_bert
https://github.com/wchh127/yykf
https://github.com/wedeling/EasySurrogate
Deep active subspaces - a scalable method for high-dimensional uncertainty propagation
https://github.com/wenbihan/vidosat_icip2015
VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising
https://github.com/wentao99/pix2pixHD
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/whisperspeech/whisperspeech
https://github.com/whr94621/NJUNMT-pytorch
https://github.com/why2000/DuReader-bert
https://github.com/will-wang19/Image-Edit-with-Mask-generated-by-Stable-Diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/willsharpless/deepreach
DeepReach: A Deep Learning Approach to High-Dimensional Reachability
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.