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/leeyegy/SimDR
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/leeyegy/simcc
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/leftthomas/restormer
Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/lehinevych/SAAN
https://github.com/lehoanganh298/BERT-Question-Answering
https://github.com/leminhbinh0209/add
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/lemon-liley/isabella
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/lenacabrera/gb_mnmt
https://github.com/lennonzurich/lalala
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/gpvit
GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/hornet
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/nfnets
High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/leshanbog/lasertagger
https://github.com/lewisluk/BoundaryAwareNetwork
Boundary-Aware Network for Fast and High-Accuracy Portrait Segmentation
https://github.com/lferry007/LargeVis
https://github.com/liampetti/DDPG
https://github.com/lidongliang666/cv_deep_learning
https://github.com/lif31up/attention-is-all-you-need
https://github.com/lif31up/extended-BERT-for-low-rank-adaption
https://github.com/lightning-uq-box/lightning-uq-box
ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference
https://github.com/lilijiangg/autodiffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/lily-x/combinatorial-rmab
https://github.com/liminn/icnet-pytorch
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/linlina/mlminf
Statistical significance in high-dimensional linear mixed models
https://github.com/liqichen6688/duo-attention
https://github.com/lisilin013/ICNet-tensorflow-ros
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/littell/NamedTransformer
https://github.com/liuch37/semantic-segmentation
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/liuluyang530/XNNPACK-gpt
https://github.com/liuyuezhang/pyrl
https://github.com/liuzeming01/durecdial
Towards Conversational Recommendation over Multi-Type Dialogs
https://github.com/liuzhen03/ADNet
Attention-guided Network for Ghost-free High Dynamic Range Imaging
https://github.com/lixilinx/psgd_tf
https://github.com/llx666/git_demo
https://github.com/logpai/loglizer
Anomaly Detection using Autoencoders in High Performance Computing Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.