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.
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VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
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Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass
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Convolutional Two-Stream Network Fusion for Video Action Recognition
https://github.com/tomdyer10/fake_news
https://github.com/tommasopuccetti/adversarial_perturbation
https://github.com/toni-sm/skrl
https://github.com/towhee-io/towhee
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
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SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
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Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/tyxr/bert
https://github.com/u7javed/Transformer-Multi-Language-Translator
https://github.com/ubc-vision/DwNet
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/umeiko/mindspore-seq2seq
https://github.com/uoguelph-mlrg/instance_selection_for_gans
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/ustcljw/fupugec-score
https://github.com/uvictor-git/fake_news_detection
Deep Two-path Semi-supervised Learning for Fake News Detection
https://github.com/uzh-rpg/rpg_e2vid
High Speed and High Dynamic Range Video with an Event Camera
https://github.com/uzi0espil/research-papers-implementation/tree/master/Attention%20Is%20All%20You%20Need
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https://github.com/v1viswan/Domain_adaptation_in_HRNet
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/vainf/diff-pruning
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/valentinmace/noisy-text
https://github.com/vanpersie32/Multigpu-Bert
https://github.com/vanslerry/long_text
https://github.com/varun-affinsys/Monocular-Depth-Estimation-with-Transfer-Learning-pretrained-MobileNetV2
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/vballoli/nfnets-pytorch
High-Performance Large-Scale Image Recognition Without Normalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.