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/mattiasxu/Video-VQVAE
https://github.com/maxjcohen/transformer
https://github.com/maxmatical/pytorch-projects
https://github.com/maxudong1993/mybert
https://github.com/mayoor/attention_network_experiments
https://github.com/mayuelala/followyourpose
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/mayurnewase/Quora-Bert
https://github.com/mayurnewase/Translation
https://github.com/mcgridles/LENS
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/mdt48/semantic-segmentation-pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/megvii-research/basecls/tree/main/zoo/public/hrnet
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/mehdibenamorr/Token-Positional-Bias
https://github.com/mehdibenamorr/bert-positional-bias-ner
https://github.com/meizi1114/bert
https://github.com/melikaemami/Generalization-Error-of-GLMs
Generalization Error of Generalized Linear Models in High Dimensions
https://github.com/metavoiceio/metavoice-src
https://github.com/mevers/animated_dimensionality_reduction
https://github.com/mhamedLmarbouh/Attention-is-all-you-need
https://github.com/mhaseeb123/DeepMSim
https://github.com/michaelpatrickpurcell/graph-dp
Differentially-Private Two-Party Egocentric Betweenness Centrality
https://github.com/michaeltrs/DeepSatData
Context-self contrastive pretraining for crop type semantic segmentation
https://github.com/microcoder-py/attn-is-all-you-need
https://github.com/microndla/mobilevitv3
https://github.com/microsoft/art
MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval
https://github.com/microsoft/onnxruntime-training-examples
https://github.com/microsoft/ptgnn
https://github.com/microsoft/visual-chatgpt
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/midas-research/calling-out-bluff
https://github.com/mightypirate1/DRL-Tetris
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/mikecheninoulu/Emotional-gesture-papers
https://github.com/milmor/latent-diffusion-transformer
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/mim-solutions/bert_for_longer_texts
https://github.com/mim-solutions/roberta_for_longer_texts
https://github.com/mindslab-ai/faceshifter
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/mindslab-ai/hififace
HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.