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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https://github.com/Asthestarsfalll/IBNNet-MegEngine
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/AtluriNikhil/Image-Captioning
https://github.com/Aveek-Saha/Transformer
https://github.com/BalajiAJ/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/BasileBron/Artificial-Intelligence-Lexical
https://github.com/Belval/NRTR
https://github.com/Bhavnicksm/vanilla-transformer-jax
https://github.com/Bigpingping97/ICNet
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/BirgerMoell/tmh
Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings
https://github.com/BlossomingL/compression_tool
https://github.com/Blue-Giant/SubspaceDNN_tf1
Subspace Decomposition based DNN algorithm for elliptic-type multi-scale PDEs
https://github.com/Boyiliee/PONO
Underwater Image Enhancement via Learning Water Type Desensitized Representations
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https://github.com/Brook1711/RIS_components
https://github.com/BryanPlummer/cite
Learning Two-Branch Neural Networks for Image-Text Matching Tasks
https://github.com/Burf/HRNetV2-OCR-Tensorflow2
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/Burf/tfdetection
https://github.com/CASIA-IVA-Lab/ISP-reID
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/CFWP/rags2ridges
The Spectral Condition Number Plot for Regularization Parameter Determination
https://github.com/CGCL-codes/naturalcc
https://github.com/CODEJIN/HiFiSinger
HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis
https://github.com/COMP6248-Reproducability-Challenge/selfsupervised-denoising
https://github.com/CSAILVision/semantic-segmentation-pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/CVxTz/music_genre_classification
https://github.com/Caesarzhang/bert-zh
https://github.com/Calylyli/Mindsporehrnet
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/Chang-Chia-Chi/SaintPlus-Knowledge-Tracing-Pytorch
https://github.com/CharlieDinh/DONE
DONE: Distributed Approximate Newton-type Method for Federated Edge Learning
https://github.com/Charliesgithub20221030/BERT
https://github.com/ChiCheng123/SRN
Selective Refinement Network for High Performance Face Detection
https://github.com/Chinnu1103/Machine-Translation-using-Transformers
https://github.com/Choddeok/EmoSpherepp
https://github.com/Chonwai/Learning_BERT
https://github.com/ChristinaRunkel/HighSpeedImaging
A Bit Too Much? High Speed Imaging from Sparse Photon Counts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.