https://github.com/nseungwoo/depolarized-holography
Neural Étendue Expander for Ultra-Wide-Angle High-Fidelity Holographic Display
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/nsfzyzz/generalization_metrics_for_nlp
https://github.com/ntkien1904/LHGNN
https://github.com/nttcslab/composing-general-audio-repr
https://github.com/ntustison/CV
Interpretable, similarity-driven multi-view embeddings from high-dimensional biomedical data
https://github.com/nunezpaul/MNIST
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
https://github.com/nv-tlabs/drivegan_code
DriveGAN: Towards a Controllable High-Quality Neural Simulation
https://github.com/nv-tlabs/editGAN_release
https://github.com/nvshrao/Bidirectional-Transformer
https://github.com/oandrienko/fast-semantic-segmentation
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/okkteam/Transformer-Transducer
https://github.com/olaviinha/NeuralImageSuperResolution
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/ololo123321/maestro
https://github.com/omerlux/NLP-PTB
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
https://github.com/omerlux/Recurrent_Neural_Network_-_Part_2
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
https://github.com/oncescuandreea/audio-retrieval
QuerYD: A video dataset with high-quality text and audio narrations
https://github.com/open-edge-platform/geti
https://github.com/open-edge-platform/training_extensions
https://github.com/open-mmlab/mmclassification
https://github.com/open-mmlab/mmdetection
https://github.com/open-mmlab/mmgeneration
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/open-mmlab/mmpose
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/open-mmlab/mmsegmentation
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/opennmt/ctranslate2
https://github.com/ortec/euro-neurips-vrp-2022-quickstart
PILS: Exploring high-order neighborhoods by pattern mining and injection
https://github.com/osmr/imgclsmob
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/owainwest/uniprot_bert
https://github.com/owj0421/DeepFashion
https://github.com/owj0421/csa-net
https://github.com/oylz/FDSSTTest
https://github.com/paolanu/BERT_epitope
https://github.com/parcorelab/comscribe
https://github.com/parilo/rl-server
https://github.com/parsa-abbasi/IUST-Pattern-Recognition
https://github.com/pat-coady/trpo
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/patrickhart/jaxdl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.