Papers with Code 아카이브
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/ankanbhunia/Handwriting-Transformers
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/ankurbhatia24/MULTIMODAL-EMOTION-RECOGNITION
https://github.com/annxingyuan/XNNPACK
https://github.com/anshky/HR-NET
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/antoinecollas/transformer_neural_machine_translation
https://github.com/anton-bushuiev/ppiformer
https://github.com/antoninschrab/dpkernel-paper
https://github.com/antoninschrab/mmdagg-paper
https://github.com/antonymarion/fit_and_compare
https://github.com/anuscode/nrms
https://github.com/anusii/graph-dp
Differentially-Private Two-Party Egocentric Betweenness Centrality
https://github.com/appcoreopc/berty
https://github.com/apple/corenet
https://github.com/appleternity/generating-figure-captions-as-a-text-summarization-task
Generalization Error of Generalized Linear Models in High Dimensions
https://github.com/aris-ai/Audio-and-text-based-emotion-recognition
Attention Based Fully Convolutional Network for Speech Emotion Recognition
https://github.com/arkel23/PyTorch-Pretrained-ViT
https://github.com/arne-cl/feng-hirst-rst-parser
Two-pass Discourse Segmentation with Pairing and Global Features
https://github.com/arnomoonens/yarll
https://github.com/artem-gorodetskii/wikiart-latent-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/asfathermou/human-computer-interaction
https://github.com/asultan123/DL_LLC_Prefetching
https://github.com/augustgw/early-exit-transformer
https://github.com/austinxy/giraffehd
https://github.com/autobotasia/vibert
https://github.com/avi33/StyleMelGan-Unofficial
StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization
https://github.com/avinashpaliwal/Super-SloMo
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
https://github.com/avinashpaul2012/neophyte
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/avivelor/UnityMachineLearningForProjectButterfly
https://github.com/awslabs/sockeye
https://github.com/ayanglab/cs2
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/ayanglab/hdl
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/ays-dev/keras-transformer
https://github.com/ayulockin/Explore-NFNet
High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/b06b01073/continuous-control
https://github.com/bGhorbani/Lazy-Training-Neural-Nets
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.