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/cpm0722/transformer_pytorch
https://github.com/craftGBD/caffe-GBD
https://github.com/cran/riskParityPortfolio
A Fast Algorithm for Computing High-dimensional Risk Parity Portfolios
https://github.com/cristinalunaj/MMEmotionRecognition
https://github.com/crowd-ai-lab/generating-figure-captions-as-a-text-summarization-task
Generalization Error of Generalized Linear Models in High Dimensions
https://github.com/cruiseresearchgroup/DroTrack
DroTrack: High-speed Drone-based Object Tracking Under Uncertainty
https://github.com/cstorm125/thai2fit
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
https://github.com/cuber2460/bert
https://github.com/cvpaperchallenge/FashionCultureDataBase_DLoader
https://github.com/cyb1212/Monocular-Depth-Estimation-vis-Transfer-learning-Winter-school-
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/cyberzhg/keras-pos-embd
https://github.com/czero69/acomoeye-NN
https://github.com/d-shivam/Pose-estimation-based-action-recognition-for-help-Situation-Identification
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/daheyinyin/ICNet
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/damien911224/theWorldInSafety
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/dan-lennox/ml-udacity-quadcopter-rl
https://github.com/daniallin/DenseDepth-Pytorch
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/danini/homography-from-sift-features
Homography from two orientation- and scale-covariant features
https://github.com/danrugeles/Heron
SAME but Different: Fast and High-Quality Gibbs Parameter Estimation
https://github.com/darkrush/meta_learn
https://github.com/datacrisis/BOBBY2
https://github.com/david-yoon/multimodal-speech-emotion
https://github.com/davidalbertonogueira/NLP-tutorials
https://github.com/davidzhou9/DeepVol
Solving high-dimensional partial differential equations using deep learning
https://github.com/davisking/dlib-models
https://github.com/dccuchile/CC6204
https://github.com/dchetelat/acer
https://github.com/deepjyotisaha85/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/deepmind/dd_two_player_games
https://github.com/deepmind/discretisation_drift
https://github.com/deepmind/haiku
https://github.com/deepmipt/bert/tree/feat/multi_gpu
https://github.com/deforum/stable-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/demelin/nematode
https://github.com/denis19973/faceshifter_tornado
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.