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/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture
https://github.com/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture/blob/main/Transformer.py
https://github.com/abhaskumarsinha/MinimalGPT
https://github.com/abhi1kumar/hrnet_pose_single_gpu
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/abhi8585/GeneratedART-NFT-VISION
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/abhiagg92/bert_sentiment
https://github.com/abhilashreddys/Fake-News-Article
https://github.com/abmitra84/Machine_Translation
https://github.com/adi2103/AML-CoVe
https://github.com/adik993/ppo-pytorch
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/adsieg/Speech
Multimodal Speech Emotion Recognition and Ambiguity Resolution
https://github.com/advimman/lama
Feature Refinement to Improve High Resolution Image Inpainting
https://github.com/af-ai-center/bert
https://github.com/af-ai-center/nerblackbox
https://github.com/agentdr1/la_mil
https://github.com/agermanidis/pix2pixHD-runway
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/ahmetumutdurmus/awd-lstm
https://github.com/ahsanabbas123/Transformer
https://github.com/ahsanmemon/NFNets-Tensorflow
High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/ai-forever/Kandinsky-2
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
https://github.com/ai-forever/kandinskyvideo
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
https://github.com/ai-forever/movqgan
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
https://github.com/ai-winter/python_motion_planning
https://github.com/ai4finance-foundation/finrl-meta
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
https://github.com/ailab-pku/rl-framework
https://github.com/aim-uofa/AdelaiDet
BoxInst: High-Performance Instance Segmentation with Box Annotations
https://github.com/aim-uofa/DiverGen
https://github.com/aim-uofa/adet
BoxInst: High-Performance Instance Segmentation with Box Annotations
https://github.com/aimbsg/EVA4_S15
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/ajbrock/BigGAN-PyTorch
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/akanyaani/minGPTF
https://github.com/akashe/Python-Code-Generation
https://github.com/akoepke/audio-retrieval-benchmark
QuerYD: A video dataset with high-quality text and audio narrations
https://github.com/akreiss/HighDimRD
Inference in Regression Discontinuity Designs with High-Dimensional Covariates
https://github.com/alathiya/RL-Quadcoptor-Flying
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.